CN114492945A - Short-term photovoltaic power prediction method, medium and equipment in electric power market background - Google Patents
Short-term photovoltaic power prediction method, medium and equipment in electric power market background Download PDFInfo
- Publication number
- CN114492945A CN114492945A CN202111679639.3A CN202111679639A CN114492945A CN 114492945 A CN114492945 A CN 114492945A CN 202111679639 A CN202111679639 A CN 202111679639A CN 114492945 A CN114492945 A CN 114492945A
- Authority
- CN
- China
- Prior art keywords
- model
- prediction
- photovoltaic power
- data
- weather
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000010248 power generation Methods 0.000 claims abstract description 33
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 29
- 238000012549 training Methods 0.000 claims abstract description 24
- 230000005611 electricity Effects 0.000 claims abstract description 17
- 230000006870 function Effects 0.000 claims description 24
- 238000005070 sampling Methods 0.000 claims description 18
- 238000004590 computer program Methods 0.000 claims description 15
- 230000004913 activation Effects 0.000 claims description 10
- 238000002790 cross-validation Methods 0.000 claims description 9
- 238000003860 storage Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000013277 forecasting method Methods 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 238000005457 optimization Methods 0.000 claims description 4
- 238000009825 accumulation Methods 0.000 claims description 3
- 230000008878 coupling Effects 0.000 claims description 3
- 238000010168 coupling process Methods 0.000 claims description 3
- 238000005859 coupling reaction Methods 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 3
- 238000010200 validation analysis Methods 0.000 claims description 3
- 230000007246 mechanism Effects 0.000 abstract description 15
- 230000008901 benefit Effects 0.000 abstract description 5
- 238000004519 manufacturing process Methods 0.000 abstract description 2
- 230000008569 process Effects 0.000 description 9
- 238000004891 communication Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 7
- 230000010354 integration Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000004927 fusion Effects 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 230000001364 causal effect Effects 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 210000002569 neuron Anatomy 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 241000251468 Actinopterygii Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013145 classification model Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000001808 coupling effect Effects 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 230000007773 growth pattern Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- General Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Game Theory and Decision Science (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- Evolutionary Computation (AREA)
- Development Economics (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Biomedical Technology (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Artificial Intelligence (AREA)
- Educational Administration (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
本发明的一种电力市场背景下的短期光伏功率预测方法、介质及设备,其中方法包括以下步骤,基于NWP数值,将天气分为晴天、阴天、阵雨和全雨四种天气模型;判断待测日天气类型,根据待测日天气类型选择最近的相似日光伏功率历史数据和历史NWP数值;将归类后的数据集输入Stacking集成模型的一层预测模型中的各基学习器;获得各基学习器的预测结果,构建新的训练集并输入二层预测模型;获得二层元学习器的预测结果,即为最终光伏发电功率预测值。本发明采用联合机理模型和数据驱动的算法可以有效降低预测对数据的依赖,在数据质量和数量不理想的情况下,可以兼顾预测的精度、速度和可靠性,并且大幅减小实际生产中获取数据的成本,具有显著的经济效益。
A short-term photovoltaic power prediction method, medium and equipment under the background of the electricity market of the present invention, wherein the method includes the following steps: based on the NWP value, the weather is divided into four weather models: sunny, cloudy, showers and full rain; To measure the weather type of the day, select the most recent similar daily PV power historical data and historical NWP value according to the weather type of the day to be measured; input the classified data set into each basic learner in the first-layer prediction model of the Stacking integrated model; obtain each Based on the prediction result of the base learner, construct a new training set and input it into the second-layer prediction model; obtain the prediction result of the second-layer meta-learner, which is the final photovoltaic power generation power prediction value. The invention adopts the combined mechanism model and the data-driven algorithm, which can effectively reduce the dependence of prediction on data. In the case of unsatisfactory data quality and quantity, the accuracy, speed and reliability of prediction can be taken into account, and the acquisition in actual production can be greatly reduced. The cost of data has significant economic benefits.
Description
技术领域technical field
本发明涉及光伏发电技术领域,具体涉及一种电力市场背景下的短期光伏功率预测方法、介质及设备。The invention relates to the technical field of photovoltaic power generation, in particular to a short-term photovoltaic power prediction method, medium and equipment under the background of the electricity market.
背景技术Background technique
精确地光伏功率预测在电力系统调度发挥着至关重要的作用,针对此要求,本发明开展电力市场背景下的短期光伏功率预测方法研究。Accurate photovoltaic power prediction plays a crucial role in power system scheduling. In response to this requirement, the present invention conducts research on short-term photovoltaic power prediction methods in the context of the power market.
基于数据驱动的光伏发电预测方法建模简单、算法成熟、计算速度快,但完全基于数值计算,高度依赖于数据质量和数量,也未考虑光伏发电的内部机理,预测结果的可靠性较差。而机理驱动方法可以很好地反映光伏内部发电原理和耦合作用,能够解析光伏发电的物理本质,模型具有很高的可靠性和普适性,但存在模型复杂度高、计算难度大、模型参数时变性强等缺点。目前工程中广泛应有的数据驱动预测方法,高度依赖于数据质量和数量,而获取新能源发电系统全面且合格的数据往往代价高昂。The data-driven photovoltaic power generation forecasting method has simple modeling, mature algorithm and fast calculation speed, but it is completely based on numerical calculation, highly dependent on the quality and quantity of data, and does not consider the internal mechanism of photovoltaic power generation, so the reliability of the prediction results is poor. The mechanism-driven method can well reflect the internal power generation principle and coupling effect of photovoltaics, and can analyze the physical nature of photovoltaic power generation. Disadvantages such as strong time-varying. Data-driven forecasting methods, which are widely used in engineering, are highly dependent on data quality and quantity, and obtaining comprehensive and qualified data for new energy power generation systems is often costly.
文献[1]提出了一个基于在线更新的改进晴空功率模型,针对小波动天气下的光伏功率作出超短期预测,对小波动天气在3~4h尺度下的预测精度由较好提升。文献[2]提出了一种基于自适应模糊时间序列的光伏出力预测组合模型,采用自适应算法处理历史功率数据,然后经过聚类、论域定义并划分、模糊化数据,最后结合模糊时间序列方法进行预测,并对结果去模糊化,在实际光伏实验系统上使用时间间隔为15min的数据上进行仿真,平均绝对误差(MAE)值和平均绝对百分比误差(EMAPE)值分别达到1.038MW、13.34%。文献[3]根据光伏出力与外部环境因素间的相关性,采用ExtraTreesRegressor方法进行特征重要性评估,对数据进行清洗、提取特征量后,采用基于人工鱼群法的SVM将气象数据分类,针对每类别的光伏发电量预测,并与传统的SVM模型对比,预测的精度得到大幅度提升。In [1], an improved clear sky power model based on online update was proposed to make ultra-short-term forecasts for photovoltaic power under small fluctuation weather, and the prediction accuracy for small fluctuation weather at the scale of 3 to 4 hours was improved. Reference [2] proposed a combined model of photovoltaic output forecasting based on adaptive fuzzy time series, which uses adaptive algorithm to process historical power data, then goes through clustering, domain definition and division, fuzzification data, and finally combines fuzzy time series. The method predicts and defuzzifies the results, and simulates the data with a time interval of 15 minutes on the actual photovoltaic experimental system. The mean absolute error (MAE) value and the mean absolute percentage error (EMAPE) value reach 1.038MW and 13.34 respectively. %. Reference [3] uses the ExtraTreesRegressor method to evaluate the importance of features according to the correlation between photovoltaic output and external environmental factors. After cleaning the data and extracting feature quantities, the SVM based on the artificial fish swarm method is used to classify the meteorological data. Compared with the traditional SVM model, the prediction accuracy has been greatly improved.
专利[4]公开了一种基于T-S型模糊神经网络的光伏发电预测系统,实现了模糊推理系统和神经网络学习的系统的有机结合,引入气象因子,有效提高了预测准确性和可靠性。专利[5]将光伏电站逐日历史处理数据聚类为K个簇,构造一个气象专业天气对应一个或多个数字标签的改进广义天气映射,克服光伏发电在非晴条件下预测准确性低的缺点。专利[6]采用天气打分机制将天气分类从而将发电数据分类,通过核密度函数求出每类数据的概率密度函数估计,以给出发电数据统计意义上的分布规律。专利[7]公开了一种光伏发电预测系统,通过检测实时天气状况以及光伏发电设备的编号,经纬度,放置角度及光伏材料类数据,运用机理模型获得预测发电量。Patent [4] discloses a photovoltaic power generation prediction system based on T-S fuzzy neural network, which realizes the organic combination of fuzzy inference system and neural network learning system, and introduces meteorological factors, which effectively improves the prediction accuracy and reliability. Patent [5] Clusters the daily historical processing data of photovoltaic power plants into K clusters, and constructs an improved generalized weather map that corresponds to one or more digital labels for meteorological professional weather, which overcomes the shortcomings of low prediction accuracy of photovoltaic power generation under non-clear conditions. . The patent [6] uses the weather scoring mechanism to classify the weather to classify the power generation data, and obtains the probability density function estimation of each type of data through the kernel density function, so as to give the distribution law of the power generation data in the statistical sense. Patent [7] discloses a photovoltaic power generation prediction system, which uses a mechanism model to obtain predicted power generation by detecting real-time weather conditions and the serial number, latitude and longitude, placement angle and photovoltaic material data of photovoltaic power generation equipment.
[1]马原,张雪敏,甄钊,等.基于修正晴空模型的超短期光伏功率预测方法[J].电力系统自动化,2021,45(11):44-51;[1] Ma Yuan, Zhang Xuemin, Zhen Zhao, et al. Ultra-short-term photovoltaic power prediction method based on modified clear sky model [J]. Automation of Electric Power Systems, 2021, 45(11): 44-51;
[2]杨志超,朱峰,张成龙,等.基于自适应模糊时间序列法的光伏发电短期功率预测[J].南京工程学院学报(自然科学版),2014,12(1):6-13;[2] Yang Zhichao, Zhu Feng, Zhang Chenglong, et al. Short-term power prediction of photovoltaic power generation based on adaptive fuzzy time series method [J]. Journal of Nanjing Institute of Technology (Natural Science Edition), 2014, 12(1): 6-13;
[3]王小杨,罗多,孙韵琳,等.基于ABC-SVM和PSO-RF的光伏微电网日发电功率组合预测方法研究[J].太阳能学报,2020,41(03):177-183;[3] Wang Xiaoyang, Luo Duo, Sun Yunlin, et al. Research on the combined prediction method of daily power generation of photovoltaic microgrid based on ABC-SVM and PSO-RF [J]. Journal of Solar Energy, 2020, 41(03): 177-183 ;
[4]陆玉正,王军,张耀明,李俊娇.一种基于T-S型模糊神经网络的光伏发电预测系统[P].江苏:CN103106544A,2013-05-15;[4] Lu Yuzheng, Wang Jun, Zhang Yaoming, Li Junjiao. A photovoltaic power generation prediction system based on T-S fuzzy neural network [P]. Jiangsu: CN103106544A, 2013-05-15;
[5]张栋梁,严健,纵兆丹,任晓达,李国欣,刘建华.基于K均值聚类改进广义天气的光伏发电预测方法[P].江苏省:CN106022538B,2020-04-07;[5] Zhang Dongliang, Yan Jian, Zong Zhaodan, Ren Xiaoda, Li Guoxin, Liu Jianhua. A photovoltaic power generation prediction method based on K-means clustering to improve generalized weather [P]. Jiangsu Province: CN106022538B, 2020-04-07;
[6]葛维春,潘霄,李家珏,张铁岩,马少华.基于形状参数置信区间的光伏发电预测方法[P].辽宁省:CN108256690B,2021-10-08;[6] Ge Weichun, Pan Xiao, Li Jiajue, Zhang Tieyan, Ma Shaohua. Prediction method of photovoltaic power generation based on confidence interval of shape parameter [P]. Liaoning Province: CN108256690B, 2021-10-08;
[7]刘乐乐,黄乐,林栋.一种光伏发电预测系统的预测方法[P].江苏省:CN106909985B,2021-02-09。[7] Liu Lele, Huang Le, Lin Dong. A prediction method of photovoltaic power generation prediction system [P]. Jiangsu Province: CN106909985B, 2021-02-09.
发明内容SUMMARY OF THE INVENTION
本发明提出的一种电力市场背景下的短期光伏功率预测方法、介质及设备,可至少解决背景技术的问题之一。The short-term photovoltaic power prediction method, medium and device under the background of the electricity market proposed by the present invention can at least solve one of the problems of the background art.
为实现上述目的,本发明采用了以下技术方案:To achieve the above object, the present invention has adopted the following technical solutions:
一种电力市场背景下的短期光伏功率预测方法,包括以下步骤,A short-term photovoltaic power forecasting method in the context of the electricity market, comprising the following steps,
基于NWP数值,将天气分为晴天、阴天、阵雨和全雨四种天气模型;Based on the NWP value, the weather is divided into four weather models: sunny, cloudy, shower and full rain;
判断待测日天气类型,根据待测日天气类型选择最近的相似日光伏功率历史数据和历史NWP数值;Determine the weather type of the day to be measured, and select the most recent similar day's historical PV power data and historical NWP value according to the weather type of the day to be measured;
将归类后的数据集输入Stacking集成模型的一层预测模型中的各基学习器;Input the classified data set into each basic learner in the one-layer prediction model of the Stacking ensemble model;
获得各基学习器的预测结果,构建新的训练集并输入二层预测模型;Obtain the prediction results of each base learner, construct a new training set and input the two-layer prediction model;
获得二层元学习器的预测结果,即为最终光伏发电功率预测值。Obtain the prediction result of the two-layer meta-learner, which is the final photovoltaic power generation power prediction value.
进一步的,基于NWP数值,将天气分为晴天、阴天、阵雨和全雨四种天气模型;具体包括:Further, based on the NWP value, the weather is divided into four weather models: sunny day, cloudy day, shower and full rain; the details include:
选取NWP中的云量C和降雨量p作为天气的分型因素,按照白天平均云量分为晴天模型和阴天模型,按照白天降雨时长分为阵雨模型和全雨模型;The cloud amount C and the rainfall p in the NWP are selected as the weather classification factors. According to the average cloud amount during the day, it is divided into a sunny model and a cloudy model, and according to the daytime rainfall duration, it is divided into a shower model and a full rain model.
晴天模型为阴天模型为阵雨模型为全雨模型为c1为晴天和阴天模型的分型阈值,t1为阵雨和全雨模型的分型阈值;根据短期天气预报国家标准及气象学原理,c1=0.7,t1=4。The sunny model is The cloudy model is The shower model is The full rain model is c 1 is the classification threshold of sunny and cloudy models, t 1 is the classification threshold of shower and full rain models; according to the national standard for short-term weather forecast and the principle of meteorology, c 1 =0.7, t 1 =4.
进一步的,所述Stacking集成模型的一层预测模型包括光伏发电的物理特性模型如公式(1)所示:Further, the one-layer prediction model of the Stacking integrated model includes the physical characteristic model of photovoltaic power generation as shown in formula (1):
P=ηSI[1-0.005(t+25)] (1)P=ηSI[1-0.005(t+25)] (1)
式中,P为光伏发电功率,η为光伏板转换效率,S为光伏板有效面积,I为辐照度,t为光伏板工作温度。In the formula, P is the photovoltaic power generation, η is the conversion efficiency of the photovoltaic panel, S is the effective area of the photovoltaic panel, I is the irradiance, and t is the operating temperature of the photovoltaic panel.
进一步的,一层预测模型还包括TCN网络预测模型,所述TCN网络预测模型构建步骤如下,Further, the one-layer prediction model also includes a TCN network prediction model, and the construction steps of the TCN network prediction model are as follows,
空洞卷积步骤,每个TCN层含有L个卷积层,扩张卷积计算公式为:In the hole convolution step, each TCN layer contains L convolution layers, and the dilated convolution calculation formula is:
式中:空洞系数d=(1,…,2L),k为卷积核大小;In the formula: the hole coefficient d=(1,...,2 L ), k is the size of the convolution kernel;
残差链接步骤:Residual linking steps:
Relu表示线性整流函数,用作神经网络的激活函数;DCConv表示空洞卷积层;Relu represents the linear rectification function, which is used as the activation function of the neural network; DCConv represents the hole convolution layer;
公式(3)、(4)表示TCN的激活函数:Formulas (3) and (4) represent the activation function of TCN:
式中:W(1)、W(2)为对应输入的权重矩阵,b为偏置向量,S(i,j)表示第j块第i层的激活函数,公式(3)表示t时刻空洞卷积的结果,公式(4)表示加入残差链接后的结果。In the formula: W (1) and W (2) are the weight matrices corresponding to the input, b is the bias vector, S (i, j) represents the activation function of the i-th layer of the j-th block, and formula (3) represents the hole at time t The result of the convolution, formula (4) represents the result after adding the residual link.
进一步的,获得各基学习器的预测结果,构建新的训练集并输入二层预测模型,采用以下算法:Further, the prediction results of each base learner are obtained, a new training set is constructed and a two-layer prediction model is input, using the following algorithm:
采用LightGBM模型基于直方图的算法来缓解高维数据对预测的影响;LightGBM模型采用带深度限制的Leaf-wise算法进行优化;The histogram-based algorithm of the LightGBM model is used to alleviate the impact of high-dimensional data on prediction; the LightGBM model is optimized by the Leaf-wise algorithm with depth limitation;
LightGBM模型的目标函数如下:The objective function of the LightGBM model is as follows:
Obj(t)=L(t)+Ω(t)+c (5)Obj(t)=L(t)+Ω(t)+c (5)
式中:Obj(t)为优化目标,Ω(t)表示正则函数,反映模型的复杂度;t表示采样时间;c表示额外参数,避免过拟合并优化树深度;In the formula: Obj(t) is the optimization objective, Ω(t) is the regular function, which reflects the complexity of the model; t is the sampling time; c is the extra parameter to avoid overfitting and optimize the tree depth;
L(t)表示损失函数,通过描述了N个采样点的实际值yi和预测值的比较来反映模型的拟合度;定义如下:L(t) represents the loss function, which describes the actual value yi and predicted value of N sampling points to reflect the fit of the model; the definition is as follows:
通过对回归树进行串联耦合,传输先前学习器的残差信息;最终输出由剩余树的累加生成。Residual information of previous learners is transmitted by serial coupling of regression trees; final output Generated by the accumulation of the remaining trees.
进一步的,获得各基学习器的预测结果,构建新的训练集并输入二层预测模型,还采用以下算法:Further, the prediction results of each base learner are obtained, a new training set is constructed and input into the two-layer prediction model, and the following algorithm is also used:
采用基于梯度的单边采样算法GOSS,在取样时,GOSS会将满足条件的大梯度样本全部保留作为被采样的数据,而对梯度较小的样本采取随机采样的方式。The gradient-based unilateral sampling algorithm GOSS is adopted. When sampling, GOSS will retain all the large gradient samples that meet the conditions as the sampled data, and adopt random sampling for the samples with smaller gradients.
进一步的,获得各基学习器的预测结果,构建新的训练集并输入二层预测模型,还采用以下算法,Further, the prediction results of each base learner are obtained, a new training set is constructed and a two-layer prediction model is input, and the following algorithm is also used:
采用了互斥特征绑定的方法EFB,GOSS采样后,会用绑定互斥特征的方式来减少特征的维度以防止维度灾并提升计算效率。The mutually exclusive feature binding method EFB is adopted. After GOSS sampling, it will use the method of binding mutually exclusive features to reduce the dimension of features to prevent dimension disaster and improve computational efficiency.
进一步的,Stacking集成模型的一层预测模型通过交叉验证将数据集进行划分为多个子集,并对评估结果进行融合,降低模型预测结果的方差,提高模型的泛化能力,避免过拟合现象的发生;Further, the one-layer prediction model of the Stacking ensemble model divides the data set into multiple subsets through cross-validation, and fuses the evaluation results to reduce the variance of the model prediction results, improve the generalization ability of the model, and avoid overfitting. happened;
K折交叉验证即将数据集平均分为K份,其中K-1份作为训练集,剩余1份为验证集;用以上K种情况的训练集训练得到模型超参数。K-fold cross-validation is to divide the data set into K parts on average, of which K-1 is used as a training set, and the remaining 1 is a validation set; the model hyperparameters are obtained by training with the training sets of the above K cases.
另一方面,本发明还公开一种计算机可读存储介质,存储有计算机程序,所述计算机程序被处理器执行时,使得所述处理器执行如上述方法的步骤。On the other hand, the present invention also discloses a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, causes the processor to execute the steps of the above method.
再一方面,本发明还公开一种计算机设备,包括存储器和处理器,所述存储器存储有计算机程序,所述计算机程序被所述处理器执行时,使得所述处理器执行如上述方法的步骤。In yet another aspect, the present invention also discloses a computer device, comprising a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method .
由上述技术方案可知,为克服现有技术不足,本发明提出一种计及机理模型的多模型融合Stacking集成学习方式的光伏功率预测方法。考虑不同算法的数据观测与训练原理差异,充分发挥各个模型优势,构建多个机器学习算法并结合光伏机理模型嵌入的Stacking集成学习的光伏预测模型,模型的基学习器包含光伏物理模型,LightGBM算法和时间卷积网络算法。It can be seen from the above technical solutions that, in order to overcome the deficiencies of the prior art, the present invention proposes a photovoltaic power prediction method with a multi-model fusion Stacking integrated learning method taking into account the mechanism model. Considering the differences in data observation and training principles of different algorithms, give full play to the advantages of each model, build multiple machine learning algorithms and combine the photovoltaic prediction model of Stacking integrated learning embedded in the photovoltaic mechanism model. The basic learners of the model include photovoltaic physical model, LightGBM algorithm and temporal convolutional network algorithms.
本发明提出的一种电力市场背景下的短期光伏功率预测方法,采用联合机理模型和数据驱动的算法可以有效降低预测对数据的依赖,在数据质量和数量不理想的情况下,可以兼顾预测的精度、速度和可靠性,并且大幅减小实际生产中获取数据的成本,具有显著的经济效益。同时,可以有效提升新能源短期预测的精度,在调度计划制定、电力市场交易、负荷管理等方面发挥重要支持,进一步提升电网安全经济运行水平,也有助于提升电网公司对新能源发电企业和用户的并网服务品质。The short-term photovoltaic power prediction method under the background of the power market proposed by the present invention can effectively reduce the dependence of prediction on data by using a joint mechanism model and a data-driven algorithm. In the case of unsatisfactory data quality and quantity, the prediction can be Accuracy, speed and reliability, and greatly reduce the cost of data acquisition in actual production, with significant economic benefits. At the same time, it can effectively improve the accuracy of short-term forecasting of new energy, and play an important role in the formulation of dispatch plans, power market transactions, load management, etc. quality of grid-connected services.
附图说明Description of drawings
图1是本发明实施例的机理模型与数据驱动联合光伏功率预测流程图;1 is a flow chart of a mechanism model and a data-driven combined photovoltaic power prediction according to an embodiment of the present invention;
图2是时间卷积网络结构示意图;Figure 2 is a schematic diagram of a time convolutional network structure;
图3是残差链接原理图;Figure 3 is a schematic diagram of residual link;
图4是直方图算法原理图;Figure 4 is a schematic diagram of the histogram algorithm;
图5是Leaf-wise生长方式图;Fig. 5 is a Leaf-wise growth pattern diagram;
图6是Stacking集成模型原理图;Figure 6 is the schematic diagram of the Stacking integration model;
图7是本实施例交叉验证示意图。FIG. 7 is a schematic diagram of cross-validation in this embodiment.
具体实施方式Detailed ways
为使本发明实施例的目的、技术方案和优点更加清楚,下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例是本发明一部分实施例,而不是全部的实施例。In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, but not all embodiments.
本实施例所述的电力市场背景下的短期光伏功率预测方法,具体如下:The short-term photovoltaic power prediction method in the context of the electricity market described in this embodiment is as follows:
1.1机理模型与数据驱动联合光伏发电预测1.1 Mechanism model and data-driven combined photovoltaic power generation forecast
1.1.1基于NWP的天气分型1.1.1 Weather classification based on NWP
光伏电站发电功率受当日所接收太阳辐照度的影响,而所接收的太阳辐照度受到天气类型的影响,不同天气下的光伏发电功率波动不同。选取NWP中的云量C和降雨量p作为天气的分型因素。按照白天平均云量分为晴天模型和阴天模型,按照白天降雨时长t分为阵雨模型和全雨模型。The photovoltaic power generation power is affected by the received solar irradiance on the day, and the received solar irradiance is affected by the weather type, and the photovoltaic power generation power fluctuates differently in different weathers. The cloud amount C and the rainfall p in the NWP were selected as weather classification factors. According to the average cloud cover during the day It is divided into a sunny model and a cloudy model, and is divided into a shower model and a full rain model according to the rainfall duration t during the day.
表1天气分型模型Table 1 Weather classification model
表1中,c1为晴天和阴天模型的分型阈值,t1为阵雨和全雨模型的分型阈值。根据短期天气预报国家标准及气象学原理,c1=0.7,t1=4。In Table 1, c 1 is the classification threshold for sunny and cloudy models, and t 1 is the classification threshold for shower and full rain models. According to the national standard for short-term weather forecast and the principle of meteorology, c 1 =0.7, t 1 =4.
1.1.2计及机理模型的Stacking多模型融合短期光伏发电预测1.1.2 Stacking multi-model fusion short-term photovoltaic power generation prediction considering the mechanism model
基于上述原理介绍,将光伏物理发电模型嵌入Stacking集成模型的一层预测模型中,设为基学习器之一,构建计及机理模型的Stacking多模型融合光伏发电预测模型。下面给出短期光伏发电功率预测的实现过程,流程图如图1所示。Based on the above principle introduction, the photovoltaic physical power generation model is embedded in the first-level prediction model of the Stacking integrated model, and it is set as one of the basic learners to construct a Stacking multi-model fusion photovoltaic power generation prediction model that takes into account the mechanism model. The realization process of short-term photovoltaic power generation power prediction is given below, and the flow chart is shown in Figure 1.
步骤如下:Proceed as follows:
(1)基于NWP数值,将天气分为晴天、阴天、阵雨和全雨四种天气模型。(1) Based on the NWP value, the weather is divided into four weather models: sunny, cloudy, shower and full rain.
(2)判断待测日天气类型,根据待测日天气类型选择最近的相似日光伏功率历史数据和历史NWP数值。(2) Judge the weather type of the day to be measured, and select the recent similar day's PV power historical data and historical NWP value according to the weather type of the to-be-measured day.
(3)将归类后的数据集输入Stacking集成模型的一层预测模型中的各基学习器。(3) Input the classified data set into each basic learner in the one-layer prediction model of the Stacking ensemble model.
(4)获得各基学习器的预测结果,构建新的训练集并输入二层预测模型。(4) Obtain the prediction results of each base learner, construct a new training set and input the two-layer prediction model.
(5)获得二层元学习器的预测结果,即为最终光伏发电功率预测值。(5) Obtain the prediction result of the two-layer meta-learner, which is the final photovoltaic power generation power prediction value.
1.2光伏发电物理模型1.2 Physical model of photovoltaic power generation
光伏发电的物理模型基于太阳辐射与光电转换特性来对发电功率进行预测。光伏发电的物理特性模型如公式(1)所示。The physical model of photovoltaic power generation predicts the generated power based on the characteristics of solar radiation and photoelectric conversion. The physical characteristic model of photovoltaic power generation is shown in formula (1).
P=ηSI[1-0.005(t+25)] (1)P=ηSI[1-0.005(t+25)] (1)
式中,P为光伏发电功率,η为光伏板转换效率,S为光伏板有效面积,I为辐照度,t为光伏板工作温度。In the formula, P is the photovoltaic power generation, η is the conversion efficiency of the photovoltaic panel, S is the effective area of the photovoltaic panel, I is the irradiance, and t is the operating temperature of the photovoltaic panel.
1.3TCN网络预测模型1.3 TCN network prediction model
TCN主要结构可分为适用于序列的因果卷积以及适用于历史数据记忆的空洞卷积加残差模块模型。由于其卷积层层之间存在因果关系,可以记忆更多的历史数据,适用于光伏电站的数据。The main structure of TCN can be divided into causal convolution suitable for sequences and atrous convolution plus residual module model suitable for historical data memory. Due to the causal relationship between its convolutional layers, it can memorize more historical data, which is suitable for the data of photovoltaic power plants.
(1)空洞卷积(1) Hole convolution
TCN结构如图2所示,每个TCN层含有L个卷积层,扩张卷积计算公式为: The TCN structure is shown in Figure 2. Each TCN layer contains L convolutional layers. The dilated convolution calculation formula is:
式中:空洞系数d=(1,…,2L),k为卷积核大小。In the formula: the hole coefficient d=(1,...,2 L ), and k is the size of the convolution kernel.
(2)残差链接(2) Residual link
图3为残差链接图,Dropout表示在神经元传播过程中,让某个神经元的激活值以一定概率停止工作,从而增强模型的泛化性。Relu表示线性整流函数,用作神经网络的激活函数;DC Conv表示空洞卷积层。Figure 3 is a residual link diagram. Dropout means that in the process of neuron propagation, the activation value of a neuron stops working with a certain probability, thereby enhancing the generalization of the model. Relu represents the linear rectification function, which is used as the activation function of the neural network; DC Conv represents the atrous convolutional layer.
公式(3)、(4)表示TCN的激活函数:Formulas (3) and (4) represent the activation function of TCN:
式中:W(1)、W(2)为对应输入的权重矩阵,b为偏置向量,S(i,j)表示第j块第i层的激活函数,公式(3)表示t时刻空洞卷积的结果,公式(4)表示加入残差链接后的结果。In the formula: W (1) and W (2) are the weight matrices corresponding to the input, b is the bias vector, S (i, j) represents the activation function of the i-th layer of the j-th block, and formula (3) represents the hole at time t The result of the convolution, formula (4) represents the result after adding the residual link.
1.4LightGBM1.4LightGBM
1.4.1直方图算法1.4.1 Histogram Algorithm
LightGBM模型采用了一种基于直方图的算法来缓解高维数据对预测的影响,提高了计算速度,避免预测模型出现过拟合的现象。直方图的基本思想包含将连续的浮点特征值转换为k个整数,得到k给“桶”(bin),并构造一个宽度为k的直方图。结构如图4所示。The LightGBM model adopts a histogram-based algorithm to alleviate the influence of high-dimensional data on prediction, improve the calculation speed, and avoid the phenomenon of over-fitting of the prediction model. The basic idea of a histogram consists of converting consecutive floating-point eigenvalues into k integers, getting k to give "bins", and constructing a histogram of width k. The structure is shown in Figure 4.
1.4.2带深度限制的Leaf-wise算法1.4.2 Leaf-wise algorithm with depth limit
优化过程中,LGBM采用Leaf-wise算法寻找合适的叶子,然后分裂,并以此循环。如图5所示。LGBM在Leaf-wise上增加深度限制,防止出现过拟合的现象。LGBM的目标函数如下:During the optimization process, LGBM uses the Leaf-wise algorithm to find suitable leaves, then splits, and repeats this cycle. As shown in Figure 5. LGBM adds a depth limit on Leaf-wise to prevent overfitting. The objective function of LGBM is as follows:
Obj(t)=L(t)+Ω(t)+c (5)Obj(t)=L(t)+Ω(t)+c (5)
式中:Obj(t)为优化目标,Ω(t)表示正则函数,反映模型的复杂度。t表示采样时间。c表示额外参数,避免过拟合并优化树深度。In the formula: Obj(t) is the optimization objective, and Ω(t) represents the regular function, which reflects the complexity of the model. t represents the sampling time. c stands for extra parameters to avoid overfitting and optimize tree depth.
L(t)表示损失函数,通过描述了N个采样点的实际值yi和预测值的比较来反映模型的拟合度。定义如下:L(t) represents the loss function, which describes the actual value yi and predicted value of N sampling points to reflect the fit of the model. Defined as follows:
通过对回归树进行串联耦合,传输先前学习器的残差信息。最终输出由剩余树的累加生成。Residual information from previous learners is transmitted by serially coupling regression trees. final output Generated by the accumulation of the remaining trees.
1.4.3单边梯度采样算法1.4.3 Unilateral Gradient Sampling Algorithm
采用基于梯度的单边采样算法(GOSS)。在取样时,GOSS会将满足条件的大梯度样本全部保留作为被采样的数据,而对梯度较小的样本采取随机采样的方式,这样既保留了训练不足的数据能在下次训练中得到更多的关注,同时也不会使得样本分布产生巨大的变化。Gradient-based one-sided sampling algorithm (GOSS) is used. When sampling, GOSS will retain all the large gradient samples that meet the conditions as the sampled data, and randomly sample the samples with smaller gradients, which not only retains the insufficient training data, but can get more in the next training. At the same time, it will not make a huge change in the sample distribution.
1.4.4互斥特征捆绑算法1.4.4 Mutually Exclusive Feature Binding Algorithm
采用了互斥特征绑定的方法(EFB)。GOSS采样后,会用绑定互斥特征的方式来减少特征的维度以防止维度灾并提升计算效率。因为高维的数据大多是稀疏数据,其特征空间中的特征大多都是互斥的,可以将互斥的特征绑定在一起形成新的特征来减少特征维度。The method of mutually exclusive feature binding (EFB) is adopted. After GOSS sampling, it will use the method of binding mutually exclusive features to reduce the dimension of features to prevent dimensional disaster and improve computing efficiency. Because most high-dimensional data are sparse data, most of the features in the feature space are mutually exclusive, and the mutually exclusive features can be bound together to form new features to reduce the feature dimension.
1.5Stacking集成学习框架1.5Stacking Integrated Learning Framework
1.5.1Stacking集成模型1.5.1Stacking Integration Model
Stacking集成模型将原始数据集划分为若干子数据集,并输入至一层预测模型的各个基学习器,各基学习器预测输出各自结果形成新的数据集输入至二层预测模型进行训练,预测并输出最终结果,结构如图6所示。Stacking集成模型对多个模型输出的输出结果泛化,学习出特征之间组合的信息,有效提高整体预测精度。The Stacking ensemble model divides the original data set into several sub-data sets, which are input to each basic learner of the first-layer prediction model. And output the final result, the structure is shown in Figure 6. The Stacking ensemble model generalizes the output results of multiple models, learns the information about the combination of features, and effectively improves the overall prediction accuracy.
1.5.2交叉验证1.5.2 Cross-Validation
交叉验证通常用于评估模型的预测性能。Stacking集成模型的一层预测模型通过交叉验证将数据集进行划分为多个子集,并对评估结果进行融合,降低模型预测结果的方差,提高模型的泛化能力,避免过拟合现象的发生。K折交叉验证即将数据集平均分为K份,其中K-1份作为训练集,剩余1份为验证集。用以上K种情况的训练集训练得到模型超参数。以4折为例,过程如图7所示。Cross-validation is often used to evaluate the predictive performance of a model. The one-layer prediction model of the Stacking ensemble model divides the data set into multiple subsets through cross-validation, and fuses the evaluation results to reduce the variance of the model prediction results, improve the generalization ability of the model, and avoid the occurrence of overfitting. K-fold cross-validation is to divide the data set into K parts on average, of which K-1 is used as the training set and the remaining 1 is the validation set. The model hyperparameters are obtained by training with the training set of the above K cases. Taking 4 fold as an example, the process is shown in Figure 7.
由上可知,本发明的机理与数据联合驱动预测模式,可以结合领域先验知识模型和数据驱动的深度学习算法。应用电力知识模型,优化模型参数,提高模型适应性;提高数据驱动中机器学习和数据挖掘效率,在不提高训练样本数量的前提下降低机器学习泛化风险。将机理模型与数据驱动模型有效结合,实现了规则与经验的有机融合,可以更好地综合两种模型的优点,采用更少的数据样本和更简化的机理模型,即可实现更优的综合性能,保证良好的预测精度和效率,并有效提升预测结果的可靠性。It can be seen from the above that the mechanism of the present invention and the data-driven prediction mode can be combined with the domain prior knowledge model and the data-driven deep learning algorithm. Apply the power knowledge model, optimize model parameters, and improve model adaptability; improve the efficiency of data-driven machine learning and data mining, and reduce the risk of machine learning generalization without increasing the number of training samples. The effective combination of the mechanism model and the data-driven model realizes the organic integration of rules and experience, which can better integrate the advantages of the two models, and can achieve better synthesis by using fewer data samples and a more simplified mechanism model. performance, ensure good prediction accuracy and efficiency, and effectively improve the reliability of prediction results.
又一方面,本发明还公开一种计算机可读存储介质,存储有计算机程序,所述计算机程序被处理器执行时,使得所述处理器执行如上述电力市场背景下的短期光伏功率预测方法的步骤。In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the processor causes the processor to execute the method for short-term photovoltaic power prediction in the context of the above electricity market. step.
再一方面,本发明还公开一种计算机设备,包括存储器和处理器,所述存储器存储有计算机程序,所述计算机程序被所述处理器执行时,使得所述处理器执行如上述电力市场背景下的短期光伏功率预测方法的步骤。In yet another aspect, the present invention also discloses a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to execute the above-mentioned electricity market background The steps of the short-term photovoltaic power prediction method below.
可理解的是,本发明实施例提供的系统与本发明实施例提供的方法相对应,相关内容的解释、举例和有益效果可以参考上述方法中的相应部分。It is understandable that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and reference may be made to the corresponding part of the above-mentioned method for explanation, examples and beneficial effects of related content.
本申请实施例还提供了一种电子设备,包括处理器、通信接口、存储器和通信总线,其中,处理器,通信接口,存储器通过通信总线完成相互间的通信,Embodiments of the present application further provide an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus,
存储器,用于存放计算机程序;memory for storing computer programs;
处理器,用于执行存储器上所存放的程序时,实现上述电力市场背景下的短期光伏功率预测方法,所述方法包括:The processor is configured to implement the short-term photovoltaic power prediction method under the background of the electricity market when executing the program stored in the memory, and the method includes:
基于NWP数值,将天气分为晴天、阴天、阵雨和全雨四种天气模型;Based on the NWP value, the weather is divided into four weather models: sunny, cloudy, shower and full rain;
判断待测日天气类型,根据待测日天气类型选择最近的相似日光伏功率历史数据和历史NWP数值;Determine the weather type of the day to be measured, and select the most recent similar day's historical PV power data and historical NWP value according to the weather type of the day to be measured;
将归类后的数据集输入Stacking集成模型的一层预测模型中的各基学习器;Input the classified data set into each basic learner in the one-layer prediction model of the Stacking ensemble model;
获得各基学习器的预测结果,构建新的训练集并输入二层预测模型;Obtain the prediction results of each base learner, construct a new training set and input the two-layer prediction model;
获得二层元学习器的预测结果,即为最终光伏发电功率预测值。Obtain the prediction result of the two-layer meta-learner, which is the final photovoltaic power generation power prediction value.
上述电子设备提到的通信总线可以是外设部件互连标准(英文:PeripheralComponent Interconnect,简称:PCI)总线或扩展工业标准结构(英文:Extended IndustryStandard Architecture,简称:EISA)总线等。该通信总线可以分为地址总线、数据总线、控制总线等。The communication bus mentioned in the above electronic device may be a Peripheral Component Interconnect (English: Peripheral Component Interconnect, abbreviated: PCI) bus or an Extended Industry Standard Architecture (English: Extended Industry Standard Architecture, abbreviated: EISA) bus or the like. The communication bus can be divided into an address bus, a data bus, a control bus, and the like.
通信接口用于上述电子设备与其他设备之间的通信。The communication interface is used for communication between the above electronic device and other devices.
存储器可以包括随机存取存储器(英文:Random Access Memory,简称:RAM),也可以包括非易失性存储器(英文:Non-Volatile Memory,简称:NVM),例如至少一个磁盘存储器。可选的,存储器还可以是至少一个位于远离前述处理器的存储装置。The memory may include random access memory (English: Random Access Memory, short: RAM), or may include non-volatile memory (English: Non-Volatile Memory, short: NVM), for example, at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.
上述的处理器可以是通用处理器,包括中央处理器(英文:Central ProcessingUnit,简称:CPU)、网络处理器(英文:Network Processor,简称:NP)等;还可以是数字信号处理器(英文:Digital Signal Processing,简称:DSP)、专用集成电路(英文:ApplicationSpecific Integrated Circuit,简称:ASIC)、现场可编程门阵列(英文:Field-Programmable Gate Array,简称:FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。The above-mentioned processor may be a general-purpose processor, including a central processing unit (English: Central Processing Unit, referred to as: CPU), a network processor (English: Network Processor, referred to as: NP), etc.; or a digital signal processor (English: Digital Signal Processing, referred to as DSP), application specific integrated circuit (English: ApplicationSpecific Integrated Circuit, referred to as: ASIC), Field Programmable Gate Array (English: Field-Programmable Gate Array, referred to as: FPGA) or other programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
在本申请提供的又一实施例中,还提供了一种包含指令的计算机程序产品,当其在计算机上运行时,使得计算机执行上述实施例中任一电力市场背景下的短期光伏功率预测方法。In yet another embodiment provided by the present application, a computer program product including instructions is also provided, which, when running on a computer, causes the computer to execute the short-term photovoltaic power forecasting method in the context of any electricity market in the above-mentioned embodiments .
在上述实施例中,可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时,可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行所述计算机程序指令时,全部或部分地产生按照本申请实施例所述的流程或功能。所述计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。所述计算机指令可以存储在计算机可读存储介质中,或者从一个计算机可读存储介质向另一个计算机可读存储介质传输,例如,所述计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线(DSL))或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。所述计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集成的服务器、数据中心等数据存储设备。所述可用介质可以是磁性介质,(例如,软盘、硬盘、磁带)、光介质(例如,DVD)、或者半导体介质(例如固态硬盘Solid State Disk(SSD))等。In the above-mentioned embodiments, it may be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented in software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer may be a general purpose computer, special purpose computer, computer network, or other programmable device. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be downloaded from a website site, computer, server, or data center Transmission to another website site, computer, server, or data center is by wire (eg, coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (eg, infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, a data center, or the like that includes an integration of one or more available media. The usable media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, DVDs), or semiconductor media (eg, Solid State Disk (SSD)), among others.
需要说明的是,在本文中,诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来,而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且,术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含,从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素,而且还包括没有明确列出的其他要素,或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下,由语句“包括一个……”限定的要素,并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any relationship between these entities or operations. any such actual relationship or sequence exists. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device that includes a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.
本说明书中的各个实施例均采用相关的方式描述,各个实施例之间相同相似的部分互相参见即可,每个实施例重点说明的都是与其他实施例的不同之处。尤其,对于系统实施例而言,由于其基本相似于方法实施例,所以描述的比较简单,相关之处参见方法实施例的部分说明即可。Each embodiment in this specification is described in a related manner, and the same and similar parts between the various embodiments may be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for related parts, please refer to the partial descriptions of the method embodiments.
以上实施例仅用以说明本发明的技术方案,而非对其限制;尽管参照前述实施例对本发明进行了详细的说明,本领域的普通技术人员应当理解:其依然可以对前述各实施例所记载的技术方案进行修改,或者对其中部分技术特征进行等同替换;而这些修改或者替换,并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围。The above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The recorded technical solutions are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202111679639.3A CN114492945A (en) | 2021-12-31 | 2021-12-31 | Short-term photovoltaic power prediction method, medium and equipment in electric power market background |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202111679639.3A CN114492945A (en) | 2021-12-31 | 2021-12-31 | Short-term photovoltaic power prediction method, medium and equipment in electric power market background |
Publications (1)
Publication Number | Publication Date |
---|---|
CN114492945A true CN114492945A (en) | 2022-05-13 |
Family
ID=81510180
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202111679639.3A Pending CN114492945A (en) | 2021-12-31 | 2021-12-31 | Short-term photovoltaic power prediction method, medium and equipment in electric power market background |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN114492945A (en) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114971058A (en) * | 2022-06-09 | 2022-08-30 | 哈尔滨工业大学 | Photovoltaic forecasting method based on depth attention network and clear sky radiation prior fusion |
CN116017936A (en) * | 2022-12-06 | 2023-04-25 | 北京纪新泰富机电技术股份有限公司 | Control method and device for air conditioner room, electronic equipment and storage medium |
CN119150253A (en) * | 2024-11-18 | 2024-12-17 | 杭州致成电子科技有限公司 | Photovoltaic maximum output power prediction method, system, electronic equipment and storage medium |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109685257A (en) * | 2018-12-13 | 2019-04-26 | 国网青海省电力公司 | A kind of photovoltaic power generation power prediction method based on Support vector regression |
CN110766134A (en) * | 2019-09-25 | 2020-02-07 | 福州大学 | Short-term power prediction method of photovoltaic power station based on recurrent neural network |
CN112561058A (en) * | 2020-12-15 | 2021-03-26 | 广东工业大学 | Short-term photovoltaic power prediction method based on Stacking-ensemble learning |
KR20210156654A (en) * | 2020-06-18 | 2021-12-27 | 한국전력공사 | Stacking Ensemble Type Short-term Power Demand Prediction Method and Apparatus |
-
2021
- 2021-12-31 CN CN202111679639.3A patent/CN114492945A/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109685257A (en) * | 2018-12-13 | 2019-04-26 | 国网青海省电力公司 | A kind of photovoltaic power generation power prediction method based on Support vector regression |
CN110766134A (en) * | 2019-09-25 | 2020-02-07 | 福州大学 | Short-term power prediction method of photovoltaic power station based on recurrent neural network |
KR20210156654A (en) * | 2020-06-18 | 2021-12-27 | 한국전력공사 | Stacking Ensemble Type Short-term Power Demand Prediction Method and Apparatus |
CN112561058A (en) * | 2020-12-15 | 2021-03-26 | 广东工业大学 | Short-term photovoltaic power prediction method based on Stacking-ensemble learning |
Non-Patent Citations (2)
Title |
---|
杨建英;: "基于自动分发多级分解TCN-BiLSTM-LightGBM家庭PV发电量预测", 电子制作, no. 19, 27 September 2020 (2020-09-27), pages 69 - 71 * |
杨荣新;孙朝云;徐磊;: "基于Stacking模型融合的光伏发电功率预测", 计算机系统应用, no. 05, 15 May 2020 (2020-05-15), pages 36 - 45 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114971058A (en) * | 2022-06-09 | 2022-08-30 | 哈尔滨工业大学 | Photovoltaic forecasting method based on depth attention network and clear sky radiation prior fusion |
CN116017936A (en) * | 2022-12-06 | 2023-04-25 | 北京纪新泰富机电技术股份有限公司 | Control method and device for air conditioner room, electronic equipment and storage medium |
CN119150253A (en) * | 2024-11-18 | 2024-12-17 | 杭州致成电子科技有限公司 | Photovoltaic maximum output power prediction method, system, electronic equipment and storage medium |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN109214566B (en) | Short-term forecasting method of wind power based on long short-term memory network | |
CN108280551B (en) | Photovoltaic power generation power prediction method utilizing long-term and short-term memory network | |
CN110705743B (en) | New energy consumption electric quantity prediction method based on long-term and short-term memory neural network | |
CN114492945A (en) | Short-term photovoltaic power prediction method, medium and equipment in electric power market background | |
CN108985965A (en) | A kind of photovoltaic power interval prediction method of combination neural network and parameter Estimation | |
CN105654207A (en) | Wind power prediction method based on wind speed information and wind direction information | |
Liu et al. | Photovoltaic generation power prediction research based on high quality context ontology and gated recurrent neural network | |
CN111915092B (en) | Ultra-short-term wind power forecasting method based on long-short-term memory neural network | |
CN113554466A (en) | Construction method, forecasting method and device for short-term electricity consumption forecasting model | |
CN109840633B (en) | Photovoltaic output power prediction method, system and storage medium | |
CN110390436A (en) | Short-term prediction method for coal storage amount of power plant based on SSA and LSTM deep learning | |
Al-Rousan et al. | Assessment of predicting hourly global solar radiation in Jordan based on Rules, Trees, Meta, Lazy and Function prediction methods | |
CN114792156A (en) | Photovoltaic output power prediction method and system based on clustering of curve characteristic indexes | |
CN113822418A (en) | Wind power plant power prediction method, system, device and storage medium | |
CN110866633A (en) | An ultra-short-term load forecasting method for microgrid based on SVR support vector regression | |
CN109002926A (en) | The photovoltaic power generation quantity prediction model and its construction method of a kind of high accuracy and application | |
CN110852492A (en) | An ultra-short-term forecasting method for photovoltaic power based on Mahalanobis distance | |
Syu et al. | Ultra-short-term wind speed forecasting for wind power based on gated recurrent unit | |
CN116826737A (en) | A photovoltaic power prediction method, device, storage medium and equipment | |
CN115238948A (en) | Method and device for predicting power generation capacity of small hydropower station | |
CN118134046A (en) | Wind farm power prediction method and system based on machine learning | |
CN118228888A (en) | Photovoltaic power prediction method and system based on deep hybrid kernel extreme learning machine model | |
CN115271242A (en) | Training method, prediction method and device for photovoltaic power generation power prediction model | |
CN115238854A (en) | Short-term load prediction method based on TCN-LSTM-AM | |
Zim et al. | Short-term weather forecasting for wind energy generation using a deep learning technique |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination |