PJM电力市场及upppcl提前一小时负荷预测

Kishan Bhushan Sahay, Vishesh Rana
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引用次数: 2

摘要

短期负荷预测是电力系统规划、运行和控制的重要手段。许多运行决策都是基于负荷预测,如发电容量调度调度、可靠性分析和发电机维护计划等。本文讨论了人工智能(AI)在短期负荷预测(STLF)中的重要作用,即提前一小时预测电力系统负荷。设计了一种新的人工神经网络(ANN)来计算预测负荷。历史电力负荷数据被用于人工神经网络的建模。人工神经网络模型在PJM电力市场和upppcl的每小时数据上进行训练,并在样本外数据上进行测试。仿真结果表明,基于该方法的一小时负荷预报精度高,误差小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
One hour ahead load forecast of PJM electricity market & UPPCL
Short-term load forecasting is an essential instrument in power system planning, operation & control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis & maintenance planning for the generators. This paper discusses significant role of artificial intelligence (AI) in short-term load forecasting (STLF), that is, the one hour-ahead forecast of the power system load. A new artificial neural network (ANN) has been designed to compute the forecasted load. Historical electricity load data has been used in the modeling of ANN. The ANN model is trained on hourly data from PJM Electricity Market & UPPCL and tested on out-of-sample data. Simulation results obtained have shown that one hour-ahead forecasts of load using proposed ANN is very accurate with very less error.
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