Abstract
The improvement of load forecasting accuracy is an important issue in the scientific optimization of power systems. The availability of accurate statistical data and a suitable scientific method are necessary for a perfect prediction of future occurrences. This research deals with the use of a regression forecast model (Support Vector Machine, SVM) for the prediction of the vector data for electrical power loading and temperature in Baghdad city. The Firefly algorithm was used to optimize the parameters of the SVM to improve its prediction accuracy. The quantitative statistical performance evaluation measures (absolute proportional error (MAPE)) were used to evaluate the performance of the optimization methods. The results proved that the modification method was more accurate compared to the basic method and PSO-SVM.
Highlights
In the context of increasing power demand and development of the power market, load forecasting is a major challenge in terms of power system planning and operation [13]
A comparison of the absolute proportional error between Support Vector Machines (SVM), PSO-SVM, and the proposed methods was performed, and the results showed that the proposed method was more efficient in prediction compared to the other methods in terms of forecasting the system within a short duration
The aim of the work is to examine the capability of the proposed Firefly algorithm (FA)-SVM in reducing the mean absolute error percentage (MAPE) in high complex prediction problems
Summary
In the context of increasing power demand and development of the power market, load forecasting is a major challenge in terms of power system planning and operation [13]. The advantage of a precise load forecasting is to help the operators make decisions for the commitment unit, reduce the reserve capacity, and make a proper schedule for maintenance planning [4]. Apart from holiday forecasting, the power system experts have divided forecasting into long, medium, and shortterm forecasting based on the duration of the term. The intelligent methods of power system forecasting have been increased significantly over the last decades to reduce the error in the expected power system changes [5,6,7,8,9]. Bayesian Neural Networks, Deep neural networks, and Deep neural networks have been assumed as a feasible method for presaging the power in this year [7,8,9]
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