Forecasting day, week and month ahead electricity load consumption of a building using empirical mode decomposition and extreme learning machine

Abstract

Forecasting of building energy consumption plays a key role in the energy management of the modern power system. However, the noise and randomness in the electricity load data makes it difficult to forecast accurate electricity load. In this paper, a novel scheme namely Empirical Mode Decomposition based Extreme Learning Machine (EMD-ELM) is proposed to forecast the electricity load consumption of a building. Randomness in the electric load data is removed using EMD, whereas, ELM is used to forecast the day, week and month ahead electricity load. To illustrate the usefulness of EMD-ELM, the performance is compared with the renowned neural networks namely Convolution Neural Network (CNN), Long Short Term Memory (LSTM) and ELM. The simulation results clearly indicate that EMD-ELM outperforms CNN, LSTM and ELM in forecasting the day, week and month ahead electricity load consumption of a building.