Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting

As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability

Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc.), which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network (ANN) that performs Short-Term Load Forecasting (STLF). In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. 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A fuzzy logic controller based mid-term load forecasting with renewable penetration in Assam, India

An accurate mid-term load forecasting (MTLF) tool is an essential part of power systems planning and sustainable development. In order to compensate the extra uncertainties, the power systems with high renewable penetration need even more accurate MTLF tool. The electric load demand is highly prejudiced by the thermal inertia due to the local climatic factors. Therefore, the accuracy of an MTLF method is highly dependent on the incorporated climatic factors. This paper proposes a fuzzy logic comptroller based MTLF method with renewable penetration. In order to achieve a higher degree of forecasting accuracy proposed method incorporated several climatic factors in the forecasting process. The study is done in Assam, a state of India and the proposed method is utilized to forecast the daily average load demand for one month. The forecasting accuracy of the proposed method is compared with one of most commonly used tool for MTLF called artificial neural network (ANN). The empirical results affirm the superiority of the proposed method over the ANN