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Forecasting monthly electric load and energy for a fast growing utility using an artificial neural network

机译:使用人工神经网络预测快速增长的公用事业的每月电力负荷和能源

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In this paper, novel artificial neural network (ANN) based weather-load and weather-energy models have been developed to forecast electric load and energy for 24 months ahead. A set of weather and other variables which have been identified for both models together with their correlations and contribution to the forecasted variable is reported. The proposed ANN models have been applied to historical energy, load, and weather data available for the Muscat power system from 1986 to 1990. Forecast results, when compared with the actual data for 1991-1992, show that monthly electric energy and load can be predicted within a maximum error of 6% and 10%, respectively, even with forecasted weather. The proposed ANN models provide better accuracy than previously developed models.
机译:在本文中,已经开发了基于新型人工神经网络(ANN)的天气负荷和天气能量模型来预测未来24个月的电力负荷和能源。报告了为这两个模型确定的一组天气和其他变量,以及它们的相关性和对预测变量的贡献。拟议的ANN模型已应用于1986年至1990年可用于马斯喀特电力系统的历史能源,负荷和天气数据。与1991-1992年的实际数据相比,预测结果表明,每月的电能和负荷可以即使在天气预报的情况下,其最大误差也分别在6%和10%之内。拟议的人工神经网络模型提供了比以前开发的模型更好的准确性。

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