Short-term forecasting of residential electricity demand using CNN-LSTM

Near accurate forecasting of energy demand has become a non-trivial requirement for developing effective management and planning strategies/policies for a resilient energy system. This paper is aimed to develop a novel deep learning-based energy demand prediction model by utilising the combination of Convolutional neural networks and Long Short-term Memory units. The proposed model consists of two one dimensional convolutional layer with max pooling, two bidirectional LSTM layers and finally three fully connected dense layer. The energy consumption data available for a household based in Findhorn ecovillage located in the north of Scotland for a six-week period during the February and March of 2015 was utilised to train, validate, and test the models. The proposed model provides energy demand prediction for short-term forecasting (5 minutes). The results obtained from the model are compared against four of the classical and widely applied algorithms for time series forecasting: autoregressive integrated moving average (ARIMA), light gradient boosting machine (LightGBM), random forest (RF), and deep neural networks (DNN). The result obtained demonstrated the efficiency of the proposed architecture in outperforming all well-established models.