Fuzzy-Swarm Intelligence-Based Short-Term Load Forecasting Model as a Solution to Power Quality Issues Existing in Microgrid System

Abstract

Load demand is highly stochastic and uncertain. This is because it was highly influenced by a number of variables like load type, weather conditions, time of day, the seasonality factor, economic constraints, and other randomness effects. The loads are categorized as holiday loads (national and religious), weekdays, and weekend days. The nonlinearity and uncertain characteristics of electrical load in a microgrid are one of the major sources of power quality problems in a microgrid system, and they can be handled using an accurate load forecast model. The fuzzy load prediction model can effectively handle these nonlinearity and uncertainty characteristics to have an accurate load forecast, but the main challenge with this model is its inability to accommodate a large volume of historical load and weather information when the membership function of the input and output fuzzy variables and the number of the fuzzy rules are tremendous. The swarm intelligence load forecast model based on particle swarm optimization algorithms can improve the limitations of the fuzzy system and increase its forecasting performance. The parameters of time, temperature, historical load, and error correction factors are considered as the Fuzzy and Fuzzy-PSO model input variables, while the forecasted industrial load is the only output variable. The Gaussian membership function is considered for both the input and output fuzzy variables. A 3-year historical hourly load data of an Ethiopian industrial system is used to train and validate both prediction models. The mean absolute percentage error (MAPE) is used to evaluate the performance of these prediction models. The Fuzzy-PSO load prediction model shows results that have superior performance to the fuzzy-alone load prediction results.

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A Levenberg–Marquardt Based Neural Network for Short-Term Load Forecasting

“…Past research efforts focused on classical and computational intelligence techniques for load forecasting [5]. Both methods have shortcomings; for example, the classical techniques have a limited ability to handle nonlinear data.…”