Day-ahead energy forecasting for a water utility using multiple linear regression analysis and artificial neural networks - Tagros, Christian Morallos (2013)
The increasing cost of electricity impacts the cost of water. The implementation of Open Access and Retail Competition (OARC) in the Philippine Electric Power Industry adds more pressure to Water Utilities to choose the appropriate electricity supplier and operate water distribution facilities more efficiently to ensure the least cost option in terms of electricity expenses. The need to accurately forecast the electricity demand cannot be over-emphasized. The use Multiple Linear Regression Analysis and Artificial Neural Network techniques were investigated in this study to generate day-ahead energy consumption forecasts for a water facility. The objective is to utilize the operating parameters in a pumping station ? historical energy and water demand data in relation to weather parameters ? high and low temperatures, and rainfall occurrence. Five Multiple Linear Regression and five ANN Forecasting Models were developed. The Mean Absolute Percentage Errors (MAPE) of the valid models was below the 5% target. The study further found that weather parameters are very good input variables in forecasting the energy consumption of a pumping station together with the historical energy and water demand date of the facility.