CN107704966A - A kind of Energy Load forecasting system and method based on weather big data - Google Patents
A kind of Energy Load forecasting system and method based on weather big data Download PDFInfo
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Abstract
The present invention provides a kind of Energy Load forecasting system and method based on weather big data, and system includes three data acquisition, model learning and load prediction modules, and method key step includes：First, the characteristics such as temperature, humidity, wind-force, rainfall and intensity of illumination are included according to Energy Load location, wherein the temperature-sensitive value of acquisition synoptic weather observation data, temperature sensor thermometric value and the smart mobile phone of local user, synoptic weather observation data；Secondly, the weather data of acquisition is normalized, forms weather big data training set；Then, weighing factor value of the weather data to Energy Load data is extracted with XGBoost gradients boosting algorithm, then using the forecast model based on LSTM neural network models structure Energy Load；Finally, with reference to the data of weather forecast of one's respective area, the Energy Load that prediction area is treated with the forecast model of Energy Load is predicted.The present invention effectively improves the single Time series analysis method of traditional Energy Load, improves Energy Load precision of prediction.
Description
Technical Field
The invention relates to the technical field of energy load prediction, in particular to an energy load prediction system and method based on weather big data.
Background
With the development of economy, the demand of various industries on energy sources such as electric power and the like is increasing. The energy loads such as electric power and the like are effectively analyzed and accurately predicted, and the production and reasonable configuration of energy sources are facilitated.
At present, most energy load prediction methods are based on time series classical processing methods, namely, a prediction model is established by analyzing historical data of energy loads, and common technologies include an ARMA model, wavelet analysis, a BP neural network and the like. Although these methods have better and better fitting effect on data, in practice, the prediction of energy load is influenced by a plurality of uncertainty factors including environmental factors, so that a large error exists in prediction accuracy.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an energy load prediction method based on weather big data, wherein weather historical data (such as temperature, humidity, wind power, rainfall, illumination intensity and the like) and historical energy load data are combined to establish a model, and weather forecast information and the model are used for prediction during prediction, so that the effect of energy load prediction is improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention discloses an energy load prediction system based on weather big data, which comprises a data acquisition module, a model learning module and a load prediction module, wherein the data acquisition module is used for acquiring weather big data; the data acquisition module is used for acquiring weather historical data and historical energy load data, the weather historical data comprises air temperature, humidity, wind power, rainfall and illumination intensity, and the energy load historical data comprises historical observation data of a meteorological bureau on a local area, a measured value of a local area temperature sensor device and a temperature sensing value of a local user smart phone; in addition, the data acquisition module is also required to acquire historical energy load data according to a time sequence;
the model learning module is used for taking the acquired data as a training data set so as to learn a model for predicting the energy load; the model for predicting the energy load is used for carrying out normalization processing on the obtained weather data to form a weather big data training set which takes temperature, humidity, wind power, rainfall and illumination intensity as characteristics and time as a sequence; acquiring historical energy load data, analyzing the correlation between the historical energy load data and the historical weather data by using a gradient lifting algorithm, and extracting weather characteristic weight values influencing different energy loads; establishing a prediction model of the energy load based on the extracted weather characteristics;
and the load prediction module is used for providing a predicted energy load result according to the prediction model of the energy load and by combining weather forecast data of the region to be predicted.
Preferably, in the load prediction module, the time-series data of the date to be predicted and the weather forecast data of the date to be predicted are used as the input of a load prediction model, and the model calculates the predicted energy load result.
The invention also discloses a forecasting method of the energy load forecasting system based on the weather big data, which comprises the following steps:
step 1: acquiring weather observation data of a weather bureau about local temperature, humidity, wind power, rainfall and illumination intensity according to an area where an energy load is located; secondly, acquiring a temperature measurement value of a temperature sensor at the location of the energy load; in addition, acquiring a temperature sensing value of the smart phone of the user in the region;
step 2: carrying out normalization processing on the obtained weather data to form a weather big data training set which takes temperature, humidity, wind power, rainfall and illumination intensity as characteristics and takes time as a sequence;
and step 3: acquiring historical energy load data, and extracting weather characteristic weight values influencing different energy loads to further form a complete characteristic data set;
and 4, step 4: establishing a prediction model of the energy load based on the extracted weather characteristics;
and 5: and predicting the energy load of the area to be predicted by combining the weather forecast information.
As an optimal technical scheme, in order to unify weather data from different sources, characteristic data of temperature, humidity, wind power, rainfall and illumination intensity are collected at certain time intervals; the meteorological bureau observation data is obtained through an application program interface API, the temperature sensor data of the place where the energy load is located is obtained through an instrument device, the temperature sensing value of the user smart phone is obtained by converting the measured temperature of the mobile phone battery or the temperature sensor data into the environmental temperature, and the conversion method adopts the following model:
in the formulaIs the average ambient temperature for a time interval,is the average cell phone battery temperature or cell phone temperature sensor temperature over the space area A and time interval h, T0Is a fixed equilibrium temperature constant, mjTo estimate the coefficients, ∈j,hFor random perturbation terms, j is the sample number.
As a preferred technical solution, in the step 2, normalization processing is performed on weather data including five characteristics of temperature, humidity, wind power, rainfall and illumination intensity, and the normalization processing method adopts the following calculation formula:
xi＝(xi-μ)/(max-min) (2)
in the formula xiAnd μ is the mean value of all sample data under the characteristic, and max and min represent the maximum value and the minimum value of the sample data under the characteristic respectively.
As a preferred technical scheme, in the step 3, weather characteristic weight values influencing different energy loads are extracted, so as to provide reference for establishing a prediction model; the XGboost algorithm is adopted to extract features, namely, the extreme gradient lifting algorithm, and the target function of the XGboost algorithm is as follows according to the idea of gradient lifting decision tree:
wherein,t represents the number of leaf nodes, wjRepresenting the weight of each leaf node, and gamma and lambda are parameters for controlling the specific gravity of different parts in the model; by making a pair of wjTaking the derivative and making it equal to 0, one can solve:
when the original training data is used as the input of the XGboost algorithm, 3 parts of data are used as characteristics: historical energy load sequence data, time sequence data and weather sequence data, wherein the historical energy load sequence data and the time sequence data are indispensable characteristics, so that the characteristic importance weights of different weather characteristics in the weather sequence data are obtained according to the XGboost algorithm; the different weather characteristics comprise temperature, humidity, wind power, rainfall and illumination intensity;
as a preferred technical scheme, in the step 4, a load prediction model is constructed by using an LSTM neural network model, and a formed matrix is used as an input, and a prediction model of the energy load is obtained by adjusting and setting appropriate LSTM neural network parameters through training.
Preferably, in step 5, weather forecast information of the forecast area is first acquired, and when the energy load is forecast, the weather forecast data of the date to be forecasted and the time series data of the date to be forecasted are used as input variables, and the load forecasting model in step 4 is used for calculation and output as a result of the forecast energy load.
Compared with the prior art, the invention has the following advantages and effects:
according to the method, historical weather data of an area where the energy load is located are obtained through multiple sources, including but not limited to meteorological bureau observation data, temperature sensor data and area user smart phone data, a weather characteristic weight value related to the energy load data is extracted by adopting an XGboost gradient lifting algorithm, the energy load data, the weather characteristic and the weight data are combined, and an LSTM neural network is used for constructing a load prediction model, so that the defect of the traditional energy load single time sequence analysis method in the prediction effect is overcome, the energy load prediction precision is effectively improved, and particularly, better robustness is reflected when the energy load is influenced by weather environmental factors and has larger fluctuation.
Drawings
FIG. 1 is a principal schematic of the present invention;
FIG. 2 is a flow chart of an energy load prediction implementation of the present invention;
FIG. 3 is a diagram of a load prediction model constructed using an LSTM-based neural network model according to the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
As shown in fig. 1, the energy load prediction system based on weather big data in the embodiment includes a data acquisition module, a model learning module and a load prediction module;
the data acquisition module is used for acquiring weather historical data and energy load historical data, the weather historical data comprises air temperature, humidity, wind power, rainfall and illumination intensity, and the energy load historical data comprises historical observation data of a meteorological bureau on a local area, a measured value of a local area temperature sensor device and a temperature sensing value of a local user smart phone; in addition, the data acquisition module is also required to acquire historical energy load data according to a time sequence;
the model learning module is used for taking the acquired data as a training data set so as to learn a model for predicting the energy load; the model for predicting the energy load is used for carrying out normalization processing on the obtained weather data to form a weather big data training set which takes temperature, humidity, wind power, rainfall and illumination intensity as characteristics and time as a sequence; acquiring historical energy load data, analyzing the correlation between the historical energy load data and the historical weather data by using an XGboost gradient lifting algorithm, and extracting weather characteristic weight values influencing different energy loads; establishing a prediction model of the energy load by using an LSTM neural network based on the extracted weather characteristics;
and the load prediction module is used for providing a predicted energy load result according to the prediction model of the energy load and by combining weather forecast data of the region to be predicted.
As shown in fig. 2, the energy load prediction method based on the weather big data includes the following steps:
step 1: acquiring weather observation data of a weather bureau about local temperature, humidity, wind power, rainfall and illumination intensity according to an area where an energy load is located; secondly, acquiring a temperature measurement value of a temperature sensor at the location of the energy load; in addition, acquiring a temperature sensing value of the smart phone of the user in the region;
step 2: carrying out normalization processing on the obtained weather data to form a weather big data training set which takes temperature, humidity, wind power, rainfall and illumination intensity as characteristics and takes time as a sequence;
and step 3: acquiring historical energy load data, and extracting weather characteristic weight values influencing different energy loads to further form a complete characteristic data set;
and 4, step 4: establishing a prediction model of the energy load based on the extracted weather characteristics;
and 5: and predicting the energy load of the area to be predicted by combining the weather forecast information.
In the step 1, in order to unify weather data from different sources, data such as air temperature, humidity, wind power, rainfall, illumination intensity and the like are collected at certain time intervals. The meteorological bureau observation data can be obtained through an Application Program Interface (API), the temperature sensor data of the energy load location can be obtained through an instrument device, and the temperature sensing value of the user smart phone is obtained by converting the measured mobile phone battery temperature or the temperature sensor data into the environmental temperature, wherein the conversion method comprises the following models:
in the formulaIs the average ambient temperature for a time interval,the average mobile phone battery temperature or the temperature of the mobile phone temperature sensor (in the space area A and the time interval h), T, of a certain time interval0Is a fixed equilibrium temperature constant, mjTo estimate the coefficients, ∈j,hFor random perturbation terms, j is the sample number.
In the step 2, the weather data including the characteristics of temperature, humidity, wind power, rainfall, illumination intensity and the like are normalized, and the normalization processing method includes, but is not limited to, the following calculation formula:
xi＝(xi-μ)/(max-min) (2)
in the formula xiFor the ith sample data, μ is the mean of all sample data under the characteristic, and max and min representMaximum and minimum values of sample data under this feature.
In the step 3, the weather characteristic weight values influencing different energy loads are extracted, and reference is further provided for building a prediction model. An XGboost (eXtreme Gradient Boosting) algorithm is adopted for feature extraction, and a target function of the XGboost algorithm is as follows according to the idea of Gradient Boosting decision trees:
wherein,t represents the number of leaf nodes, wjRepresenting the weight of each leaf node, and gamma and lambda are parameters in the model that control the specific gravity of the different parts. By making a pair of wjTaking the derivative and making it equal to 0, one can solve:
when the original training data is used as the input of the XGBoost algorithm, there are mainly 3 parts of data that can be used as features: historical energy load sequence data, time sequence data and weather sequence data are indispensable characteristics, so that the characteristic importance weights of different weather characteristics (temperature, humidity, wind power, rainfall and illumination intensity) in the weather sequence data are mainly obtained according to the XGboost algorithm.
In the step 4, a load prediction model is constructed by using an LSTM neural network model, as shown in fig. 3, a matrix composed of a time sequence, a historical energy load sequence, a weather feature sequence and a weight is used as an input, and a prediction model of the energy load can be obtained by training by adjusting and setting appropriate LSTM neural network parameters. In the LSTM neural network parameter setting process, the important parameter setting and description are as follows:
in the step 5, weather forecast information of a forecast area is firstly acquired, and when energy load forecast is performed, weather forecast data of a date to be forecasted and time series data of the date to be forecasted are used as input variables, and the load forecasting model in the step 4 is used for calculation and output as a forecast energy load result.
The above embodiments are not intended to limit the present invention, and any other changes, modifications, substitutions, combinations and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents and fall within the scope of the present invention.
Claims (8)
1. An energy load prediction system based on weather big data is characterized by comprising a data acquisition module, a model learning module and a load prediction module; the data acquisition module is used for acquiring weather historical data and historical energy load data, the weather historical data comprises air temperature, humidity, wind power, rainfall and illumination intensity, and the energy load historical data comprises historical observation data of a meteorological bureau on a local area, a measured value of a local area temperature sensor device and a temperature sensing value of a local user smart phone; in addition, the data acquisition module is also required to acquire historical energy load data according to a time sequence;
the model learning module is used for taking the acquired data as a training data set so as to learn a model for predicting the energy load; the model for predicting the energy load is used for carrying out normalization processing on the obtained weather data to form a weather big data training set which takes temperature, humidity, wind power, rainfall and illumination intensity as characteristics and time as a sequence; acquiring historical energy load data, analyzing the correlation between the historical energy load data and the historical weather data by using a gradient lifting algorithm, and extracting weather characteristic weight values influencing different energy loads; establishing a prediction model of the energy load based on the extracted weather characteristics;
and the load prediction module is used for providing a predicted energy load result according to the prediction model of the energy load and by combining weather forecast data of the region to be predicted.
2. The weather big data-based energy load prediction system according to claim 1, wherein the load prediction module takes time series data of a date to be predicted and weather forecast data of the date to be predicted as inputs of a load prediction model, and the model obtains a predicted energy load result through calculation.
3. The forecasting method of the energy load forecasting system based on the weather big data as claimed in claim 1, characterized by comprising the following steps:
step 1: acquiring weather observation data of a weather bureau about local temperature, humidity, wind power, rainfall and illumination intensity according to an area where an energy load is located; secondly, acquiring a temperature measurement value of a temperature sensor at the location of the energy load; in addition, acquiring a temperature sensing value of the smart phone of the user in the region;
step 2: carrying out normalization processing on the obtained weather data to form a weather big data training set which takes temperature, humidity, wind power, rainfall and illumination intensity as characteristics and takes time as a sequence;
and step 3: acquiring historical energy load data, and extracting weather characteristic weight values influencing different energy loads to further form a complete characteristic data set;
and 4, step 4: establishing a prediction model of the energy load based on the extracted weather characteristics;
and 5: and predicting the energy load of the area to be predicted by combining the weather forecast information.
4. The weather big data-based energy load forecasting method according to claim 3, wherein in the step 1, in order to unify weather data from different sources, characteristic data of temperature, humidity, wind power, rainfall and illumination intensity are collected at certain time intervals; the meteorological bureau observation data is obtained through an application program interface API, the temperature sensor data of the place where the energy load is located is obtained through an instrument device, the temperature sensing value of the user smart phone is obtained by converting the measured temperature of the mobile phone battery or the temperature sensor data into the environmental temperature, and the conversion method adopts the following model:
in the formulaIs the average ambient temperature for a time interval,is the average cell phone battery temperature or cell phone temperature sensor temperature over the space area A and time interval h, T0Is a fixed equilibrium temperature constant, mjTo estimate the coefficients, ∈j,hFor random perturbation terms, j is the sample number.
5. The weather big data-based energy load prediction method according to claim 3, wherein in the step 2, the weather data including five characteristics of temperature, humidity, wind power, rainfall and illumination intensity is normalized by using the following calculation formula:
xi＝(xi-μ)/(max-min) (2)
in the formula xiAnd μ is the mean value of all sample data under the characteristic, and max and min represent the maximum value and the minimum value of the sample data under the characteristic respectively.
6. The weather big data-based energy load forecasting method according to claim 3, wherein in the step 3, weather characteristic weight values affecting different energy loads are extracted to provide reference for building a forecasting model; the XGboost algorithm is adopted to extract features, namely, the extreme gradient lifting algorithm, and the target function of the XGboost algorithm is as follows according to the idea of gradient lifting decision tree:
wherein,t represents the number of leaf nodes, wjRepresenting the weight of each leaf node, and gamma and lambda are parameters for controlling the specific gravity of different parts in the model; by making a pair of wjTaking the derivative and making it equal to 0, one can solve:
when the original training data is used as the input of the XGboost algorithm, 3 parts of data are used as characteristics: historical energy load sequence data, time sequence data and weather sequence data, wherein the historical energy load sequence data and the time sequence data are indispensable characteristics, so that the characteristic importance weights of different weather characteristics in the weather sequence data are obtained according to the XGboost algorithm; the different weather characteristics include temperature, humidity, wind, rainfall and illumination intensity.
7. The weather big data-based energy load prediction method according to claim 3, wherein in the step 4, the load prediction model is constructed by using the LSTM-based neural network model, and the formed matrix is used as input, and the prediction model of the energy load is obtained by adjusting and setting appropriate LSTM neural network parameters.
8. The weather big data-based energy load prediction method according to claim 3, wherein in the step 5, weather forecast information of a prediction region is first acquired, and when energy load prediction is performed, the weather forecast data of a date to be predicted and time series data of the date to be predicted are used as input variables, and calculation is performed by using the load prediction model in the step 4, and the result is output as a predicted energy load result.
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