A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting

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Accurate energy load forecasting can not only provide favorable conditions for ensuring energy security but also reduce carbon emissions and thereby slow down the process of global warming. With the continuous construction of smart grids, energy consumption information can be easily obtained, which provides a good foundation for forecasting work. In the household energy consumption forecasting field, the current mainstream methods combine signal processing with artificial intelligence methods to improve forecasting accuracy. However, due to differences in residents' daily habits and sensor errors, the energy consumption data of individual households exhibit high volatility and contain noise. Existing methods' accuracy, robustness, generalization ability, and efficiency are challenged when considering multi-time scales and multi-samples. Based on this, this paper proposes a deep learning framework using multi-feature fusion recurrent neural networks for univariate energy consumption data. The proposed framework employs improved Singular Spectrum Analysis (SSA) and multivariate input recurrent neural network (RNN). To verify the effectiveness of the proposed framework, a series of comparative experiments were conducted on the Moroccan buildings' electricity consumption dataset (MORED). Experiments reveal that the proposed framework achieves adaptive decomposition of raw time series data and multi-feature fusion input. Moreover, under the evaluation of multiple metrics, the proposed framework outperforms the state-of-the-art methods in terms of forecasting accuracy, robustness, generalization ability, and efficiency.