Automated Short-Term Load Forecasting Using Modified Stochastic Hour Ahead Proportion (SHAP) Analysis

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) Pub Date : 2019-11-01 DOI:10.1109/HNICEM48295.2019.9073407

Adrian Jose D. Antoja, Patrick Amiel O. Lafamia, Clarizza Allen B. Yang, G. Magwili, R. Santiago

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引用次数: 3

Abstract

The accuracy of the electricity demand forecast is important in the operations of a power system. The objective of this paper is to generate an automated forecast model with better accuracy than its predecessors. The Automated ShortTerm Load Forecasting Using Modified Stochastic Hour Ahead Proportion (SHAP) Analysis is generated using the C# program. The constructed application would then use an excel spreadsheet to input data. Therefore, the data would be used as a basis for the construction of the forecast model. The output would then display the equation and the graph of the constructed forecast model. Based on the output of the program, the generated forecast model has a MAPE and SDE value of 0.941725 and 1.149855 respectively. The MAPE and SDE value of the generated forecast model is significantly lower than the MAPE and SDE values of the SHAP, WMA, SHAP-WMA which are 3.765681 and 2.822254, 5.610123 and 10.312887, 3.278946 and 3.055406 respectively. This comparison shows that the Modified SHAP Analysis is statistically better than its predecessors.

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基于改进的随机小时前比例(SHAP)分析的短期负荷自动预测

电力需求预测的准确性对电力系统的运行至关重要。本文的目标是生成一个比以前的预测模型具有更好精度的自动预测模型。使用改进的随机提前小时比例(SHAP)分析的自动短期负荷预测是使用c#程序生成的。然后，构建的应用程序将使用excel电子表格输入数据。因此，这些数据将作为构建预测模型的基础。然后输出将显示构建的预测模型的方程和图形。根据程序输出，生成的预测模型MAPE为0.941725,SDE为1.149855。生成的预测模型的MAPE和SDE值显著低于SHAP、WMA、SHAP-WMA的MAPE和SDE值(分别为3.765681和2.822254、5.610123和10.312887、3.278946和3.055406)。这一比较表明，改进的SHAP分析在统计上优于其前身。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM )

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