Electric load forecasting for large office building based on radial basis function neural network

The concept of smart grid has enabled many innovative initiatives that focus on boosting building energy efficiency such as intelligent optimal control of building energy systems and demand side management, which require accurate building load prediction. In this study, we present an hourly electric load forecasting model for large commercial office buildings based on radial basis function neural network (RBFNN) using outdoor weather data and historical load data as inputs, which is easy to implement, without tedious trial-and-error parameterizing procedures. Data from a real building under different weather conditions is used to evaluate the performance of the model and promising results are obtained, which demonstrates that the proposed method is able to precisely predict the evolving hourly electric load of the building.