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Monthly electric energy demand forecasting with neural networks and Fourier series

机译：用神经网络和傅里叶级数预测每月电力需求

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Medium-term electric energy demand forecasting is a useful tool for grid maintenance planning and market research of electric energy companies. Several methods, such as ARIMA, regression or artificial intelligence, have been usually used to carry out those predictions. Some approaches include weather or economic variables, which strongly influence electric energy demand. Economic variables usually influence the general series trend, while weather provides a periodic behavior because of its seasonal nature. This work investigates the periodic behavior of the Spanish monthly electric demand series, obtained by rejecting the trend from the consumption series. A novel hybrid approach is proposed: the periodic behavior is forecasted with a Fourier series while the trend is predicted with a neural network. Satisfactory results have been obtained, with a lower than 2% MAPE, which improve those reached when only neural networks or ARIMA were used for the same purpose.
机译：中期电能需求预测是电网维护计划和电力公司市场研究的有用工具。通常使用几种方法（例如ARIMA，回归或人工智能）来进行这些预测。一些方法包括天气或经济变量，这会严重影响电能需求。经济变量通常会影响总体序列趋势，而天气由于其季节性质而具有周期性。这项工作调查了西班牙每月电力需求序列的周期性行为，该行为是通过拒绝消费序列中的趋势而获得的。提出了一种新颖的混合方法：用傅立叶级数预测周期行为，而用神经网络预测趋势。获得了令人满意的结果，MAPE低于2％，这改善了仅将神经网络或ARIMA用于相同目的时所达到的结果。

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