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Peak load forecasting using a fuzzy neural network

机译：使用模糊神经网络的峰值负荷预测

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摘要

This paper describes electric load forecasting using a fuzzy neural network. Neural networks, though accurate in weekday load forecasting, are poor at forecasting peak loads and holiday loads. A decision system for load forecasting requires detailed analysis of data and the rule base has to be fixed heuristically for each season. The rules fixed in this way do not always yield the best forecast. This necessitates the development of a robust forecasting technique to complement the neural network to achieve a reliable forecast with improved overall accuracy. The fuzzy neural network proposed generates the fuzzy rules from historical data while learning. The adaptive rules formed this way are capable of approximating any continuous load profilc on a compact set to good accuracy. In order to evaluate the performance of the fuzzy neural network model, load forecasting is performed on a utility's data susceptible to large and sudden changes in the environmental conditions. The fuzzy neural network is compared with a neural network model on two-year utility data to obtain a one-day-ahead peak load forecast and forecast results for the months of December and May are shown to validate the effectiveness of the above approach.
机译：本文介绍了使用模糊神经网络进行电力负荷预测。神经网络尽管在工作日负荷预测中很准确，但在预测高峰负荷和假日负荷方面却很差。用于负荷预测的决策系统需要对数据进行详细分析，并且必须针对每个季节试探性地确定规则库。以这种方式确定的规则并不总是能产生最佳的预测。这就需要开发鲁棒的预测技术来补充神经网络，从而以提高的总体准确性实现可靠的预测。提出的模糊神经网络在学习的同时根据历史数据生成模糊规则。以这种方式形成的自适应规则能够以良好的精度逼近紧凑集上的任何连续负载曲线。为了评估模糊神经网络模型的性能，对容易受到环境状况的大而突然变化影响的公用事业数据进行负荷预测。将模糊神经网络与基于两年效用数据的神经网络模型进行比较，以获得提前一天的高峰负荷预测，并显示12月和5月月份的预测结果证明了上述方法的有效性。

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