A Novel Sequence to Sequence based CNN-LSTM Model for Long Term Load Forecasting

Abstract

Long term load forecasting (LTLF) models play an important role in the strategic planning of power systems around the globe. Obtaining correct decisions on power network expansions or restrictions based on future predictions help substantially reduce the infrastructure cost of the power grid. The classical approach of LTLF limits to the use of artificial neural networks (ANN) or regression based approaches along with a large set of historical demand, weather, economy and population data. Considering the drawbacks of these classical methods, this paper introduces a novel sequence to sequence (seq2seq) deep neural network (DNN) model to forecast the monthly peak demand for a time horizon of three years. Selecting the correct time interval plays a key role in LTLF. Therefore, using monthly peak demand avoids unnecessary model complications while providing all the essential information for a good long term strategical planning. The accuracy of the proposed method is verified by the load data of “New South Wales (NSW)”, Australia. The numerical results validate that the proposed method has achieved higher prediction accuracy compared to the existing work.