Enhanced ES-adRNNe Load Forecasting With Contextual Augmentation on Similar Load Days

Abstract

The importance of accurate prediction of power load variations for ensuring the reliability and rationality of power supply cannot be overstated. The GCES-adRNNe (Grey Relational Analysis Contextually Enhanced ES-adRNNe) is proposed as a load forecasting model that incorporates context augmentation on similar load days. Firstly, the selection of similar load days as representative sequences using the comprehensive grey correlation analysis method is enforced. Furthermore, context information is extracted from the representative sequences, and the individual sequences are dynamically adjusted to accommodate the main track prediction using XGBoost. Moreover, a stacked recurrent neural network (RNN) architecture with multiple layers is used. It incorporates attention-enhanced gated recurrent units (GRU) to capture various dependencies in the time series, such as short-term, long-term, and seasonal dependencies. The GRU also dynamically weight input information. Finally, the prediction results are post-processed. Experimental results indicate that the proposed model yields improvements in terms of RMSE, MAPE, StdPE, and other aspects compared to other load forecasting models. It is indicated that the model, when considering similar load days, is capable of capturing the trends and characteristics of electricity load changes more accurately, thereby providing strong support for ensuring the reliability and rationality of power supply.

Keywords

• Similar load days
• context information
• exponential smoothing
• recurrent neural network
• load forecasting