Comparing ambient temperature account methods in neural network based city short-term load forecasting

Abstract

We offer a neural network model for forecasting the next day's hourly electric load of a city. We use a few ambient temperature account methods in the research to see how each of them affects the forecasting accuracy. Optimal meta-parameters are determined to tune the neural network to give best forecasts. Among such meta-parameters are the data history depth, data seasonality radius and regularization parameter of neural network weights. A multilayer perceptron is used to make forecasts. It is shown that the electric load can be forecasted most accurately when an additional neural network forecasts hourly ambient temperatures using actual hourly temperatures of the previous day and the weather station's temperature predictions for the forecast day.