Month ahead average daily electricity price proﬁle forecasting based on a hybrid nonlinear regression and SVM model: an ERCOT case study

doi:10.1007/s40565-018-0395-3

Home > Archive>Volume 6, Issue 2, 2018 >281-291. DOI:10.1007/s40565-018-0395-3

DOI:

10.1007/s40565-018-0395-3

Author:

Ziming MA ^¹
Ziming MA
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
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Haiwang ZHONG ^¹
Haiwang ZHONG
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
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Le XIE ^²
Le XIE
Department of Electrical/Computer Engineering, Texas A&M University, College Station, TX 77843, USA
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Qing XIA ^¹
Qing XIA
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
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Chongqing KANG ^¹
Chongqing KANG
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
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Affiliation:

1. Department of Electrical Engineering, Tsinghua University, Beijing 100084, China; 2. Department of Electrical/Computer Engineering, Texas A&M University, College Station, TX 77843, USA

Fund Project:

This work was supported by National Natural Science Foundation of China (No. 51537005) and State Grid Corporation of China “Research on the model and application of power supply and demand technology under the market trading environment”.

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Abstract:

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. Offpeak 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.

Key words:Electricity price forecasting, Month ahead average daily electricity price proﬁle, Nonlinear regression model, Support vector machine (SVM), Electric reliability council of Texas (ERCOT)

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Online: March 20,2018
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