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Effect of Neural Network structure for daily electricity load forecasting

机译：神经网络结构对每日电力负荷预测的影响

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摘要

Accurate electricity demand forecasts are critical for daily operations planning. They influence many decisions, including commits to produce electricity for a given period. This paper presents a short term electricity demand forecasting system using the Artificial Neural Networks (ANNs). The model is trained and tested on 30-minutes historical load data with temperature from the Electricity Generating Authority of Thailand (EGAT) from January 1, 2012 to December 31, 2013. The ANNs use historical load data with temperature to forecast daily electricity demand in Thailand. Holidays, bridging holidays, and outliers of the raw data are detected and replaced. Historical load (previous day, previous week), forecasted day total load, forecasted day temperature, previous day temperature, calendar days (Day of week and Month), and whether the forecasted day is a holiday or not are used as input parameters. The forecasting performances are compared with Regression model. Best performance has shown with ANN.
机译：准确的电力需求预测对于日常运营计划至关重要。它们影响许多决定，包括在给定时期内发电的承诺。本文介绍了使用人工神经网络（ANN）的短期电力需求预测系统。该模型在2012年1月1日至2013年12月31日从泰国发电局（EGAT）获得的具有温度的30分钟历史负荷数据上进行了训练和测试。人工神经网络使用具有温度的历史负荷数据来预测泰国的每日电力需求。泰国。将检测并替换假期，过渡假期和原始数据的异常值。将历史负载（前一天，前一周），预测天总负载，预测天温度，前一天温度，日历天（星期几和月份）以及预测天是否为假期用作输入参数。将预测性能与回归模型进行比较。 ANN表现出最佳性能。

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