Abstract
Short-term building energy consumption forecasting is vital for energy conservation and emission reduction. However, it is challenging to achieve accurate short-term forecasting of building energy consumption due to its nonlinear and non-stationary characteristics. This paper proposes a novel hybrid short-term building energy consumption forecasting model, SSA-CNNBiGRU, which is the integration of SSA (singular spectrum analysis), a CNN (convolutional neural network), and a BiGRU (bidirectional gated recurrent unit) neural network. In the proposed SSA-CNNBiGRU model, SSA is used to decompose trend and periodic components from the original building energy consumption data to reconstruct subsequences, the CNN is used to extract deep characteristic information from each subsequence, and the BiGRU network is used to model the dynamic features extracted by the CNN for time series forecasting. The subsequence forecasting results are superimposed to obtain the predicted building energy consumption results. Real-world electricity and natural gas consumption datasets of office buildings in the UK were studied, and the multi-step ahead forecasting was carried out under three different scenarios. The simulation results indicate that the proposed model can improve building energy consumption forecasting accuracy and stability.
Highlights
Based on the above considerations, we proposed an SSA-CNNBiGRU model for shortterm prediction of building energy consumption
Compared with traditional forecasting models, the proposed model achieved the highest prediction accuracy and had stronger peak and valley capture ability, which effectively alleviated the lag of extreme point data forecasting; The simulation results demonstrated that the proposed model still had excellent forecasting precision and stability in the multi-step ahead forecasting scenario, meeting the basic building energy consumption forecasting requirements; We compared and analyzed the forecasting effects of neural network models optimized by five decomposition algorithms in the multi-step ahead forecasting scenario
Our paper proposes a SSA-CNNBiGRU model for short-term forecasting of building energy consumption
Summary
Against the background of increasing global population and rapid economic development, the energy demand for buildings has increased significantly [1]. After simulations with real-world building energy consumption datasets, we proved that the proposed model has excellent prediction accuracy and stability and can be applied to the short-term prediction of building energy consumption. We proposed a new hybrid neural network model for real-world building energy consumption forecasting based on SSA. Compared with traditional forecasting models, the proposed model achieved the highest prediction accuracy and had stronger peak and valley capture ability, which effectively alleviated the lag of extreme point data forecasting; The simulation results demonstrated that the proposed model still had excellent forecasting precision and stability in the multi-step ahead forecasting scenario, meeting the basic building energy consumption forecasting requirements; We compared and analyzed the forecasting effects of neural network models optimized by five decomposition algorithms in the multi-step ahead forecasting scenario.
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