Support vector machine for day ahead electricity price forecasting

2015 ◽  
Author(s):  
Intan Azmira binti Wan Abdul Razak ◽  
Izham bin Zainal Abidin ◽  
Yap Keem Siah ◽  
Titik Khawa binti Abdul Rahman ◽  
M. Y. Lada ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Electricity Price ◽  
Support Vector ◽  
Price Forecasting ◽  
Electricity Price Forecasting

scholarly journals Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5190 ◽  
Author(s):  
Matheus Ribeiro ◽  
Stéfano Stefenon ◽  
José de Lima ◽  
Ademir Nied ◽  
Viviana Mariani ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Electricity Price ◽  
Support Vector ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Decomposition Approach ◽  
Forecasting Models ◽  
Electricity Price Forecasting ◽  
Proposed Model ◽  
Self Adaptive

Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in the Brazilian market. Exogenous variables, such as supply, lagged prices and demand are considered as inputs signals of the forecasting model. Firstly, the coyote optimization algorithm is adopted to tune the hyperparameters of complementary ensemble empirical mode decomposition in the pre-processing phase. Next, three machine learning models, including extreme learning machine, gradient boosting machine, and support vector regression models, as well as Gaussian process, are designed with the intent of handling the components obtained through the signal decomposition approach with focus on time series forecasting. The individual forecasting models are directly integrated in order to obtain the final forecasting prices one to three-months-ahead. In this case, a grid of forecasting models is obtained. The best forecasting model is the one that has better generalization out-of-sample. The empirical results show the efficiency of the proposed model. Additionally, it can achieve forecasting errors lower than 4.2% in terms of symmetric mean absolute percentage error. The ranking of importance of the variables, from the smallest to the largest is, lagged prices, demand, and supply. This paper provided useful insights for multi-step-ahead forecasting in the electrical market, once the proposed model can enhance forecasting accuracy and stability.

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