基于神经网络算法的短期价格预测

Kishan Bhushan Sahay, Khushboo Singh
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引用次数: 1

摘要

随着电价管制的放松,电价预测受到越来越多的关注。影响天气预报的因素包括星期几、天气、季节、时间、年份和特殊日子。一般来说,驱动电价的主要因素是电力需求。本文将贝叶斯正则化、Levenberg Marquardt反向传播和缩放共轭梯度算法等人工神经网络算法应用于短期电价预测,即利用MATLAB R14a对1小时前的电价进行预测。模拟结果显示了准确的提前一小时预报。
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Short-Term Price Forecasting by Using ANN Algorithms
With deregulation much attention is given to electricity price forecasting. Some of the factors which affect forecast are day of the week, weather, season, hour of the day, year, and special days. In general, the main factor which drives the electricity price is power demand. Here, ANN algorithms i.e. Bayesian Regularization, Levenberg Marquardt back propagation & Scaled Conjugate Gradient algorithms has been applied in short-term price forecasting that is, the one hour-ahead forecast of the electricity price using MATLAB R14a. The simulation results have shown accurate one hour ahead forecasts.
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