Abstract
Short-term accurate forecasting of electricity demand is crucial for power-system operation and energy scheduling and the equilibrium between electricity generation and consumption. The predicting of electric power consumption is however difficult because electricity-demand profiles are non-linear and time varying. In this paper, we consider two hybrid deep learning architectures feedforward neural network long short-term memory (FFNN-LSTM) and feedforward neural network recurrent neural network (FFNN-RNN) for multi-horizon electricity-load forecasting. The architectures we proposed combine the ability of FFNN to represent data non-linearly with the sequential modelling capability of LSTM and RNN. The authors assess the models based on historical electricity-load observations from Ado-Ekiti, at three different forecasting horizons of 24 hours, 72 hours, and 168 hours. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) assessed predictive performance. The findings reveal that FFNN-LSTM achieves a consistently lower RMSE across the evaluated horizons, resulting in greater effectiveness to confine relatively large forecasting errors. On the other hand, the separate LSTM and RNN models achieve lower MAE and MAPE in various scenarios, suggesting stronger short-term reaction to electricity demand. The results, therefore, show a trade-off between forecast stability and sensitivity to rapid load changes. In general, the performance of hybrid architecture is more stable compared to the standalone recurrent models which are more responsive to short-term changes when we look at most of the forecasting horizons. According to the research paper, the forecasting architectures can now be selected for smart grid applications.