Electricity load forecasting and feature extraction in smart grid using neural networks

Abstract

Load forecasting plays an essential role in effective energy planning and distribution in a smart grid. However, due to the unpredictable and non-linear structure of smart grids and large datasets' complex nature, accurate load forecasting is still challenging. Statistical techniques are being used for a long time for load forecasting, but it is inefficient. This paper tries to resolve challenges imposed by conventional methods like mean and mode by suggesting an ANN model for accurate load forecasting. Specifically, the LSTM and random forest approach has been used here. We compared our model to other models that use similar parameters and found that ours is more reliable and can be used for long-term forecasting. Our model has achieved an average overall accuracy of 96% and an average MSE of 4.486 with average CPU time consumption of 904.47 s, 872.43 s, and 908.32 s, respectively. Hence, the present model outperforms other existing methods.