ARTIFICIAL NEURAL NETWORK BASE SHORT-TERM ELECTRICITY LOAD FORECASTING: A CASE STUDY OF A 132/33KV TRANSMISSION SUB-STATION
ARTIFICIAL NEURAL NETWORK BASE SHORT-TERM ELECTRICITY LOAD FORECASTING: A CASE STUDY OF A 132/33KV TRANSMISSION SUB-STATION
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基于人工神经网络的短期电力负荷预测——以132/33KV输变电站为例
DOI：
10.32479/ijeep.8629
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发表时间：
2020
影响因子：
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通讯作者：
A. Adewale
中科院分区：
文献类型：
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作者：
I. Samuel;Segun Ekundayo;A. Awelewa;T. Somefun;A. Adewale
电力负荷的预测对于任何电力系统的有效运行都是非常重要的。良好的预测结果有助于将决策风险降到最低，并降低电厂的运营成本。本文采用人工神经网络（ANN）对尼日利亚哈科特港132/33KV输电分站的短期负荷进行预测。它使用前几周的每小时负荷数据提供准确的前一周负荷预测。人工神经网络有三个部分，即；输入、处理和输出部分。输入部分有四个输入参数，它们是历史小时负荷数据（以MW为单位）、一天中的时间（以小时为单位）、一周中的天数和周末，而处理后的输出参数（即训练、验证和测试）是整个系统下一周的预测小时负荷。所采用的技术是借助MATLAB软件构建人工神经网络。r值为0.988，平均绝对偏差（MAD）为0.104，均方误差（MSE）为0.27，是一种较好的预测方法。
Forecasting of electrical load is extremely important for the effective and efficient operation of any power system. Good forecasts results help in minimizing the risk in decision making and reduces the costs of operating the power plant. This work focuses on the short-term load forecast of the 132/33KV transmission sub-station at Port-Harcourt, Nigeria, using the Artificial Neural Network (ANN). It provides accurate week-ahead load forecast using hourly load data of previous weeks. ANN has three sections namely; input, processing and output sections. There are four input parameters for the input section which are historical hourly load data (in MW), time of the day (in hours), days of the week and weekend while the output parameter after the processing (i.e. training, validation and test) is the next week hourly load predicted for the entire system. The technique used is the artificial neural network with the aid of MATLAB software. It was proven to be a good forecast method as it resulted in R-value of 0.988 which gives a mean absolute deviation (MAD) of 0.104 and mean squared error (MSE) of 0.27.