Abstract
In order to solve the problems of insufficient accuracy of long-term power load forecasting and poor applicability of the model, this paper considers the coupling of a number of macro indicators, such as regional economic development and social development indicators, with the time series data of regional power load. BP neural network and Autoregressive integrated moving average model (ARIMA) are used to integrate and improve the forecasting model, so as to improve the trend forecasting ability of annual load forecasting model. The non parametric function method is used to forecast the periodic load data in the monthly load data, the annual load forecast is combined with the monthly load forecast to improve the overall forecasting accuracy of the model. Finally, through the comparison of grey prediction and other models and the verification of MAPE error analysis method, the prediction accuracy of the model method considering the combination of data periodicity and trend is significantly improved, which is suitable for the long-term prediction of regional power load.
Highlights
The accurate prediction of long-term power load has important strategic significance for power network planning and power infrastructure construction
Long-term power load prediction is mainly based on the overall trend prediction of annual load value[1], and less consideration is given to the inertia growth of data, periodic changes and the cumulative effect of numerical values in the prediction, affecting the accuracy of load prediction
The model modeling needs to consider that the annual load growth should be based on the monthly load accumulation, so as to improve the segmenting accuracy of the model interval prediction and ensure the accuracy of the prediction
Summary
The accurate prediction of long-term power load has important strategic significance for power network planning and power infrastructure construction. In order to further improve the accuracy of long-term power load trend forecasting, the annual and monthly power load data are decomposed, Arima[6] method, which is used to study the law of data development, is integrated into BP neural network algorithm, and an improved bp-arima load trend forecasting model is proposed to realize the annual power load forecasting function under the comprehensive influence of multiple factors; the function nonparametric method is introduced to analyze the monthly data of the past years, and the periodical prediction of the future data is made by the function time series prediction model, and the component fusion of trend prediction and periodical prediction is carried out to get the new combined prediction model, so as to improve the accuracy of the long-term power load prediction
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