Postgraduate YPEC 2020

PG10 – Load Forecasting for Renewable Energy Integration

Renewable energy has long been seen as a solution to climate change. However, they introduce uncertainties in power generation as their outputs are greatly affected by weather conditions and hard to be predicted. This makes balancing the power generation and power demand becomes difficult. A more accurate load forecasting model is needed to alleviate the power system stability problem stemming from renewable integration. In this project, we proposed a 24-Parallel-Hybrid Neural Network-Robust Linear Regression ensemble approach in 24-hours ahead point load forecasting. 7-year data taken from ISO New England Inc. were used in this study. Comparing with the best commonly used approach (24-Parallel Neural Networks), the proposed ensemble approach could reduce the mean absolute percentage error by 0.5239% and improve the accuracy by 20.45%. It also demonstrated superior hourly performance in all 24 hours over the single predictor approach and 24 parallel predictors approach.