Attention Based Mechanism for Load Time Series Forecasting: AN-LSTM

Abstract

Smart grids collect high volumes of data that contain valuable information about energy consumption patterns. The data can be utilized for future strategies planning, including generation capacity and economic planning by forecasting the energy demand. In the recent years, deep learning has gained significant importance for energy load time-series forecasting applications. In this context, the current research work proposes an attention-based deep learning model to forecast energy demand. The proposed approach works by initially implementing an attention mechanism to extract relevant deriving segments of the input load series at each timestamp and assigns weights to them. Subsequently, the extracted segments are then fed to the long-short term memory network prediction model. In this way, the proposed model provides support for handling big-data temporal sequences by extracting complex hidden features of the data. The experimental evaluation of the proposed approach is conducted on the three seasonally segmented dataset of UT Chandigarh, India. Two popular performance measures (RMSE and MAPE) are used to compare the prediction results of the proposed approach with state-of-the-art prediction models (SVR and LSTM). The comparison results shows that the proposed approach outperforms other benchmark prediction models and has the lowest MAPE (7.11%).