Forecasting Energy Demand Using Conditional Random Field and Convolution Neural Network

Abstract

Electric load forecasting has been identified as an effective strategy to increase output and revenues in electrical manufacturing and distribution organizations. Several strategies for forecasting power consumption have been suggested; however, they all fail to account for small variations in power demand throughout the prediction. Therefore, the aim of this study was to develop a CRF-based power consumption prediction technique (CRF-PCP) to meet the difficulty of estimating energy consumption (EC). The EC of regions in the area is forecasted using convolution neural networks (CNNs) and conditional random fields (CRFs). Then, using the cloud, the predicted results are delivered to the electricity distribution system. To our knowledge, this is the first attempt to forecast electricity demand using CNN and CRF algorithms. In comparison to state-of-the-art algorithms, this proposed technique achieves 98.9 % accuracy. This recommended work also obtained minimum values of root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean bias error (MBE) by using 10-fold cross-validation (CV) and a hold-out (CV) methodology.