Weather big data-based energy load prediction system and method

Abstract

The present invention provides an energy load forecasting system and method based on weather big data. The system includes three modules: data acquisition, model learning and load forecasting. The main steps of the method include: firstly, according to the area where the energy load is located, the weather observation data, temperature The temperature value measured by the sensor and the temperature value of the local user's smart phone, where the weather observation data includes characteristic data such as temperature, humidity, wind, rainfall and light intensity; secondly, normalize the acquired weather data to form a weather Big data training set; then, use the XGBoost gradient boosting algorithm to extract the influence weight value of the weather data on the energy load data, and then use the LSTM neural network model to construct the energy load prediction model; finally, combined with the weather forecast data in the region, use the energy The load forecasting model predicts the energy load in the area to be forecasted. The invention effectively improves the traditional energy load single time series analysis method and improves the energy load prediction accuracy.

Description

technical field [0001] The invention relates to the technical field of energy load forecasting, in particular to an energy load forecasting system and method based on weather big data. Background technique [0002] With the development of the economy, the demand for energy such as electricity in various industries is increasing. Effective analysis and accurate forecasting of energy loads such as electricity will help energy production and rational allocation. [0003] At present, most of the energy load forecasting methods are based on the classical processing method of time series, that is, the forecasting model is established by analyzing the historical data of energy load. Common techniques include ARMA model, wavelet analysis, BP neural network, etc. Although the fitting effect of these methods on the data is getting better and better, in practice, the prediction of energy load is affected by many uncertain factors including environmental factors, so there are still lar...

AI Technical Summary

Problems solved by technology

Although the fitting effect of these methods on the data is getting better and better, in practice, the prediction of energy load is affected by many uncertain factors including environmental factors, so there is still a large error in the prediction accuracy.

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