Abstract
In the deregulated energy market, the accuracy of load forecasting has a significant effect on the planning and operational decision making of utility companies. Electric load is a random non-stationary process influenced by a number of factors which make it difficult to model. To achieve better forecasting accuracy, a wide variety of models have been proposed. These models are based on different mathematical methods and offer different features. This paper presents a new two-stage approach for short-term electrical load forecasting based on least-squares support vector machines. With the aim of improving forecasting accuracy, one more feature was added to the model feature set, the next day average load demand. As this feature is unknown for one day ahead, in the first stage, forecasting of the next day average load demand is done and then used in the model in the second stage for next day hourly load forecasting. The effectiveness of the presented model is shown on the real data of the ISO New England electricity market. The obtained results confirm the validity advantage of the proposed approach.
Highlights
With the deregulation of the energy market and the promotion of the smart grid concept, load forecasting has gained even more significance
A combined model based on the seasonal ARIMA forecasting model, the seasonal exponential smoothing model and weighted support vector machines is presented in [15] with the aim of effectively accounting for the seasonality and nonlinearity shown in the electric load
A new two-stage short-term load forecasting (STLF) approach based on least squares support vector machines with the architecture shown in Figure 5 is proposed in this paper
Summary
With the deregulation of the energy market and the promotion of the smart grid concept, load forecasting has gained even more significance. A combined model based on the seasonal ARIMA forecasting model, the seasonal exponential smoothing model and weighted support vector machines is presented in [15] with the aim of effectively accounting for the seasonality and nonlinearity shown in the electric load Another seasonal model which combines the seasonal recurrent support vector regression with a chaotic artificial bee colony algorithm is proposed in [16] to determine the appropriate values of three parameters of SVRs. In spite of all the performed research in the area of STLF, more accurate and robust load forecast methods are still required. A combined forecast model constructed as the simple average of the weather-based method, the Holt-Winters exponential smoothing and proposed method, obtained the best results at all horizons.
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