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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.
Miloš Božić; Miloš Stojanović; Zoran Stajić; Dragan Tasić. A New Two-Stage Approach to Short Term Electrical Load Forecasting. Energies 2013, 6, 2130 -2148.
AMA StyleMiloš Božić, Miloš Stojanović, Zoran Stajić, Dragan Tasić. A New Two-Stage Approach to Short Term Electrical Load Forecasting. Energies. 2013; 6 (4):2130-2148.
Chicago/Turabian StyleMiloš Božić; Miloš Stojanović; Zoran Stajić; Dragan Tasić. 2013. "A New Two-Stage Approach to Short Term Electrical Load Forecasting." Energies 6, no. 4: 2130-2148.
Providing accurate load forecast to electric utility corporations is essential in order to reduce their operational costs and increase profits. Hence, training set selection is an important preprocessing step which has to be considered in practice in order to increase the accuracy of load forecasts. The usage of mutual information (MI) has been recently proposed in regression tasks, mostly for feature selection and for identifying the real instances from training sets that contains noise and outliers. This paper proposes a methodology for the training set selection in a least squares support vector machines (LS-SVMs) load forecasting model. A new application of the concept of MI is presented for the selection of a training set based on MI computation between initial training set instances and testing set instances. Accordingly, several LS-SVMs models have been trained, based on the proposed methodology, for hourly prediction of electric load for one day ahead. The results obtained from a real-world data set indicate that the proposed method increases the accuracy of load forecasting as well as reduces the size of the initial training set needed for model training.
Miloš Božić; Milos Stojanovic; Zoran Stajič; Nenad Floranović. Mutual Information-Based Inputs Selection for Electric Load Time Series Forecasting. Entropy 2013, 15, 926 -942.
AMA StyleMiloš Božić, Milos Stojanovic, Zoran Stajič, Nenad Floranović. Mutual Information-Based Inputs Selection for Electric Load Time Series Forecasting. Entropy. 2013; 15 (3):926-942.
Chicago/Turabian StyleMiloš Božić; Milos Stojanovic; Zoran Stajič; Nenad Floranović. 2013. "Mutual Information-Based Inputs Selection for Electric Load Time Series Forecasting." Entropy 15, no. 3: 926-942.