A novel twin time series network for building energy consumption predicting
Zhixin Sun, Han Cui, Xiangxiang Mei, Hailei Yuan
TL;DR
A new model called T2SNET improves building energy consumption predictions by combining time series data with adaptive fusion techniques.
Contribution
The novel Twin Time-Series Network (T2SNET) introduces a time-embedding layer and adaptive fusion gate for better energy prediction.
Findings
T2SNET reduced MAE by 4.56%, RMSE by 9.45%, and MAPE by 3.16% on a university classroom dataset.
The model outperformed baseline methods in predicting energy consumption across various building types.
Abstract
Energy consumption prediction in buildings is crucial for optimizing energy management. The latest research faces three critical challenges: (1) Insufficient temporal correlation extraction and prediction accuracy, hindering widespread adoption and application; (2) The positive impact of timestamp embedding in time series prediction under multi-mode decomposition; and (3) The issue of adaptive coupling with multi-source data. To overcome these issues, the study proposes Twin Time-Series Networks (T2SNET), which incorporates a time-embedding layer and a Temporal Convolutional Network (TCN) to extract patterns from Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), along with an adaptive fusion gate to combine energy consumption and meteorological data. The model was evaluated on datasets from university dormitories, office buildings, and school classrooms,…
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Taxonomy
TopicsBuilding Energy and Comfort Optimization · Energy Load and Power Forecasting · Air Quality Monitoring and Forecasting