CN110991689A - 基于LSTM-Morlet模型的分布式光伏发电系统短期预测方法 - Google Patents
基于LSTM-Morlet模型的分布式光伏发电系统短期预测方法 Download PDFInfo
- Publication number
- CN110991689A CN110991689A CN201910985930.XA CN201910985930A CN110991689A CN 110991689 A CN110991689 A CN 110991689A CN 201910985930 A CN201910985930 A CN 201910985930A CN 110991689 A CN110991689 A CN 110991689A
- Authority
- CN
- China
- Prior art keywords
- model
- data
- power generation
- photovoltaic power
- lstm
- 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.)
- Granted
Links
- 238000010248 power generation Methods 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 title claims abstract description 18
- 230000000694 effects Effects 0.000 claims abstract description 14
- 238000010606 normalization Methods 0.000 claims abstract description 11
- 238000012549 training Methods 0.000 claims description 18
- 238000013145 classification model Methods 0.000 claims description 7
- 238000012360 testing method Methods 0.000 claims description 7
- 230000005855 radiation Effects 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000012544 monitoring process Methods 0.000 claims description 4
- 210000002569 neuron Anatomy 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 2
- 230000009467 reduction Effects 0.000 claims description 2
- 230000006870 function Effects 0.000 abstract description 14
- 230000004913 activation Effects 0.000 abstract description 8
- 238000013528 artificial neural network Methods 0.000 abstract description 4
- 238000002474 experimental method Methods 0.000 abstract description 2
- 230000007787 long-term memory Effects 0.000 abstract 1
- 230000006403 short-term memory Effects 0.000 abstract 1
- 230000007547 defect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 229920006395 saturated elastomer Polymers 0.000 description 1
- 239000002699 waste material Substances 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
- G06N3/044—Recurrent networks, e.g. Hopfield 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
- G06N3/04—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
- 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)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Human Resources & Organizations (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Data Mining & Analysis (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Game Theory and Decision Science (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Development Economics (AREA)
- Primary Health Care (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Photovoltaic Devices (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
Description
Claims (4)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910985930.XA CN110991689B (zh) | 2019-10-17 | 2019-10-17 | 基于LSTM-Morlet模型的分布式光伏发电系统短期预测方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910985930.XA CN110991689B (zh) | 2019-10-17 | 2019-10-17 | 基于LSTM-Morlet模型的分布式光伏发电系统短期预测方法 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN110991689A true CN110991689A (zh) | 2020-04-10 |
CN110991689B CN110991689B (zh) | 2022-10-04 |
Family
ID=70082091
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201910985930.XA Active CN110991689B (zh) | 2019-10-17 | 2019-10-17 | 基于LSTM-Morlet模型的分布式光伏发电系统短期预测方法 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN110991689B (zh) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112633604A (zh) * | 2021-01-04 | 2021-04-09 | 重庆邮电大学 | 一种基于i-lstm的短期用电量预测方法 |
CN112862630A (zh) * | 2021-03-08 | 2021-05-28 | 海南省电力学校(海南省电力技工学校) | 基于天气类型指数区间的光伏功率预测方法、终端及介质 |
CN112926772A (zh) * | 2021-02-22 | 2021-06-08 | 东南大学溧阳研究院 | 一种基于lstm-gpr混合模型的光能预测方法 |
Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107961007A (zh) * | 2018-01-05 | 2018-04-27 | 重庆邮电大学 | 一种结合卷积神经网络和长短时记忆网络的脑电识别方法 |
CN108280551A (zh) * | 2018-02-02 | 2018-07-13 | 华北电力大学 | 一种利用长短期记忆网络的光伏发电功率预测方法 |
JP6432018B1 (ja) * | 2018-02-09 | 2018-12-05 | Totalmasters株式会社 | 太陽光発電設備の施工設計支援装置、施工設計支援方法、及び施工設計支援プログラム |
CN109214575A (zh) * | 2018-09-12 | 2019-01-15 | 河海大学 | 一种基于小波长短期记忆网络的超短期风电功率预测方法 |
CN109284870A (zh) * | 2018-10-08 | 2019-01-29 | 南昌大学 | 基于长短期记忆神经网络的短期光伏发电量预测方法 |
CN109376904A (zh) * | 2018-09-18 | 2019-02-22 | 广东电网有限责任公司 | 一种基于dwt和lstm的短期风力发电功率预测方法及系统 |
CN109472404A (zh) * | 2018-10-31 | 2019-03-15 | 山东大学 | 一种电力负荷短期预测方法、模型、装置及系统 |
CN109840633A (zh) * | 2019-01-29 | 2019-06-04 | 合肥工业大学 | 光伏输出功率预测方法、系统和存储介质 |
EP3493144A1 (en) * | 2017-12-01 | 2019-06-05 | Telefonica Innovacion Alpha S.L | A method, a system and computer programs, for electrical energy distribution in a peer-to-peer distributed energy network |
CN109902874A (zh) * | 2019-02-28 | 2019-06-18 | 武汉大学 | 一种基于深度学习的微电网光伏发电短期预测方法 |
CN110188919A (zh) * | 2019-04-22 | 2019-08-30 | 武汉大学 | 一种基于长短期记忆网络的负荷预测方法 |
-
2019
- 2019-10-17 CN CN201910985930.XA patent/CN110991689B/zh active Active
Patent Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP3493144A1 (en) * | 2017-12-01 | 2019-06-05 | Telefonica Innovacion Alpha S.L | A method, a system and computer programs, for electrical energy distribution in a peer-to-peer distributed energy network |
CN107961007A (zh) * | 2018-01-05 | 2018-04-27 | 重庆邮电大学 | 一种结合卷积神经网络和长短时记忆网络的脑电识别方法 |
CN108280551A (zh) * | 2018-02-02 | 2018-07-13 | 华北电力大学 | 一种利用长短期记忆网络的光伏发电功率预测方法 |
JP6432018B1 (ja) * | 2018-02-09 | 2018-12-05 | Totalmasters株式会社 | 太陽光発電設備の施工設計支援装置、施工設計支援方法、及び施工設計支援プログラム |
CN109214575A (zh) * | 2018-09-12 | 2019-01-15 | 河海大学 | 一种基于小波长短期记忆网络的超短期风电功率预测方法 |
CN109376904A (zh) * | 2018-09-18 | 2019-02-22 | 广东电网有限责任公司 | 一种基于dwt和lstm的短期风力发电功率预测方法及系统 |
CN109284870A (zh) * | 2018-10-08 | 2019-01-29 | 南昌大学 | 基于长短期记忆神经网络的短期光伏发电量预测方法 |
CN109472404A (zh) * | 2018-10-31 | 2019-03-15 | 山东大学 | 一种电力负荷短期预测方法、模型、装置及系统 |
CN109840633A (zh) * | 2019-01-29 | 2019-06-04 | 合肥工业大学 | 光伏输出功率预测方法、系统和存储介质 |
CN109902874A (zh) * | 2019-02-28 | 2019-06-18 | 武汉大学 | 一种基于深度学习的微电网光伏发电短期预测方法 |
CN110188919A (zh) * | 2019-04-22 | 2019-08-30 | 武汉大学 | 一种基于长短期记忆网络的负荷预测方法 |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112633604A (zh) * | 2021-01-04 | 2021-04-09 | 重庆邮电大学 | 一种基于i-lstm的短期用电量预测方法 |
CN112633604B (zh) * | 2021-01-04 | 2022-04-22 | 重庆邮电大学 | 一种基于i-lstm的短期用电量预测方法 |
CN112926772A (zh) * | 2021-02-22 | 2021-06-08 | 东南大学溧阳研究院 | 一种基于lstm-gpr混合模型的光能预测方法 |
CN112862630A (zh) * | 2021-03-08 | 2021-05-28 | 海南省电力学校(海南省电力技工学校) | 基于天气类型指数区间的光伏功率预测方法、终端及介质 |
CN112862630B (zh) * | 2021-03-08 | 2024-03-22 | 海南省电力学校(海南省电力技工学校) | 基于天气类型指数区间的光伏功率预测方法、终端及介质 |
Also Published As
Publication number | Publication date |
---|---|
CN110991689B (zh) | 2022-10-04 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN109871976B (zh) | 一种基于聚类及神经网络的含分布式电源配电网电能质量预测方法 | |
CN110991786B (zh) | 基于相似日负荷曲线的10kV静态负荷模型参数辨识方法 | |
CN110929918B (zh) | 一种基于CNN和LightGBM的10kV馈线故障预测方法 | |
CN110991689B (zh) | 基于LSTM-Morlet模型的分布式光伏发电系统短期预测方法 | |
CN111369070B (zh) | 一种基于包络线聚类的多模融合光伏功率预测方法 | |
CN110674999A (zh) | 基于改进聚类和长短期记忆深度学习的小区负荷预测方法 | |
CN111753893A (zh) | 一种基于聚类和深度学习的风电机组功率集群预测方法 | |
CN111476435B (zh) | 基于密度峰值的充电桩负荷预测方法 | |
CN109978284B (zh) | 一种基于混合神经网络模型的光伏发电功率分时预测方法 | |
CN105701572B (zh) | 一种基于改进高斯过程回归的光伏短期出力预测方法 | |
CN112580588B (zh) | 一种基于经验模态分解的颤振信号智能识别方法 | |
CN109934422A (zh) | 一种基于时间序列数据分析的神经网络风速预测方法 | |
CN110717581A (zh) | 一种基于温度模糊处理和dbn的短期负荷预测方法 | |
CN111461921A (zh) | 一种基于机器学习的负荷建模典型用户数据库更新方法 | |
CN111882114B (zh) | 一种短时交通流量预测模型构建方法及预测方法 | |
CN113379116A (zh) | 基于聚类和卷积神经网络的台区线损预测方法 | |
Xie et al. | Short-term power load forecasting model based on fuzzy neural network using improved decision tree | |
CN112288157A (zh) | 一种基于模糊聚类与深度强化学习的风电场功率预测方法 | |
CN112085108A (zh) | 基于自动编码器及k均值聚类的光伏电站故障诊断算法 | |
CN115829145A (zh) | 一种光伏发电量预测系统及方法 | |
CN117132132A (zh) | 基于气象数据的光伏发电功率预测方法 | |
CN116070458A (zh) | 基于rac-gan的新建风电场场景生成方法 | |
Lu et al. | A deep belief network based model for urban haze prediction | |
Li et al. | A short-term wind power forecasting method based on NWP wind speed fluctuation division and clustering | |
CN110991743B (zh) | 一种基于聚类分析和优化神经网络的风电功率短期组合预测方法 |
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 | ||
CB03 | Change of inventor or designer information | ||
CB03 | Change of inventor or designer information |
Inventor after: Song Liangcai Inventor after: Liu Yang Inventor after: Zhu Subin Inventor after: Wang Guoqiang Inventor after: Dou Yanmei Inventor after: Suo Guilong Inventor after: Miao Xiaoyang Inventor after: Wang Xiuqing Inventor after: Cui Zhiyong Inventor after: Li Zhenji Inventor after: Zhu Yiwei Inventor after: Zhan Yong Inventor before: Song Liangcai Inventor before: Zhu Subin Inventor before: Wang Guoqiang Inventor before: Dou Yanmei Inventor before: Suo Guilong Inventor before: Wang Xiuqing Inventor before: Cui Zhiyong Inventor before: Li Zhenji Inventor before: Zhu Yiwei Inventor before: Zhan Yong Inventor before: Liu Yang |
|
GR01 | Patent grant | ||
GR01 | Patent grant |