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

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