Deep learning has been proven of great potential in various time-series forecasting applications. To exploit the potential and extendibility of deep learning in electricity load forecasting, this paper for the first time presents a comprehensive deep learning assessment on performing load forecasting at different levels through the power systems. The assessment is demonstrated via two extreme cases: 1) regional aggregated demand with an example of New England electricity load data, and 2) disaggregated household demand with examples of 100 individual households from Ireland. The state-of-the-art deep recurrent neural network is implemented for this assessment. Compared with the shallow neural network, the proposed deep model has improved the forecasting accuracy in terms of MAPE by 23% at aggregated level and RMSE by 5% at disaggregated level.
Shi, H., Xu, M., Ma, Q., Zhang, C., Li, R., & Li, F. (2017). A Whole System Assessment of Novel Deep Learning Approach on Short-Term Load Forecasting. In Energy Procedia (Vol. 142, pp. 2791–2796). Elsevier Ltd. https://doi.org/10.1016/j.egypro.2017.12.423