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首页> 外文期刊>Journal of Modern Power Systems and Clean Energy >Month ahead average daily electricity price profile forecasting based on a hybrid nonlinear regression and SVM model: an ERCOT case study
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Month ahead average daily electricity price profile forecasting based on a hybrid nonlinear regression and SVM model: an ERCOT case study

机译：基于混合非线性回归和SVM模型的未来一个月平均日电价概况预测：ERCOT案例研究

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摘要

With the deregulation of the electric power industry, electricity price forecasting plays an increasingly important role in electricity markets, especially for retailors and investment decision making. Month ahead average daily electricity price profile forecasting is proposed for the first time in this paper. A hybrid nonlinear regression and support vector machine (SVM) model is proposed. Off-peak hours, peak hours in peak months and peak hours in off-peak months are distinguished and different methods are designed to improve the forecast accuracy. A nonlinear regression model with deviation compensation is proposed to forecast the prices of off-peak hours and peak hours in off-peak months. SVM is adopted to forecast the prices of peak hours in peak months. Case studies based on data from ERCOT validate the effectiveness of the proposed hybrid method.
机译：随着电力行业的放松管制，电价预测在电力市场中扮演着越来越重要的角色，尤其是对于零售商和投资决策而言。本文首次提出了一个月的平均日电价概况预测。提出了一种混合非线性回归与支持向量机（SVM）模型。区分了非高峰时间，高峰月份的高峰时间和非高峰月份的高峰时间，并设计了不同的方法来提高预测的准确性。提出了一种具有偏差补偿的非线性回归模型，以预测非高峰时段和非高峰月份的高峰时段的价格。采用SVM来预测高峰月份的高峰时段价格。基于ERCOT数据的案例研究验证了所提出的混合方法的有效性。

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