ESAENARX and DE-RELM: Novel schemes for big data predictive analytics of electricity load and price

Abstract

Accurate forecasting of the electricity price and load is an essential and challenging task in smart grids. Since electricity load and price have a strong correlation, the forecast accuracy degrades when bidirectional relation of price and load is not considered. Therefore, this paper considers price and load relationship and proposes two Multiple Inputs Multiple Outputs (MIMO) Deep Recurrent Neural Networks (DRNNs) models for price and load forecasting. The first proposed model, Efficient Sparse Autoencoder Nonlinear Autoregressive Network with eXogenous inputs (ESAENARX) comprises of feature engineering and forecasting. For feature engineering, we propose ESAE and performed forecasting using existing method NARX. The second proposed model: Differential Evolution Recurrent Extreme Learning Machine (DE-RELM) is based on RELM model and the meta-heuristic DE optimization technique. The descriptive and predictive analyses are performed on two well-known electricity markets’ big data, i.e., ISO NE and PJM. The proposed models outperform their sub models and a benchmark model. The refined and informative features extracted by ESAE improve the forecasting accuracy in ESANARX and optimization improves the DE-RELM's accuracy. As compared to cascade Elman network, ESAENARX has reduced MAPE upto 16% for load forecasting, 7% for price forecasting. DE-RELM reduce 1% MAPE for both load and price forecasting.