journal article

Journal Article

Applied Energy. 2024, 367, p. 123379

Non-intrusive thermal load disaggregation and forecasting for effective HVAC systems

Elsevier Ltd

English

2024-08-01

https://doi.org/10.1016/j.apenergy.2024.123379

Kaneko N., Okazawa K., Zhao D., et al. Non-intrusive thermal load disaggregation and forecasting for effective HVAC systems. Applied Energy 367, 123379 (2024); https://doi.org/10.1016/j.apenergy.2024.123379.

Non-Intrusive Thermal Load Monitoring (NITLM) tracks the sub-loads generated by each heat source (e.g. occupants, equipment, solar radiation etc.) from the total thermal load and indirectly provides a room's thermal properties without additional sensors. Since sub-loads can improve the efficiency of HVAC systems, NITLM is a very attractive technology for building energy management. NITLM has traditionally focused on analyzing past and present sub-loads. However, by forecasting future sub-loads, HVAC systems will be able to schedule operations that take into account the thermal properties of future rooms. This work focuses on a new NITLM framework that forecasts future sub-loads based on the current and past total thermal loads. In experiments, we selected occupant loads that are closely connected to HVAC systems and performed sub-load forecasting using two types of approaches. One is a two-step approach that separately performs them in turn. This approach use separately trained model for disaggregation and forecasting, this allow us to fine-tuning the hyper-parameter for dedicate model. Moreover, the two-step approach can take into account the different properties and difficulties of each inference, resulting in smaller errors in sub-load forecasting. The other is an integrated approach. This approach combines load disaggregation and forecasting into a single estimation process, eliminating error propagation and reducing overall error in sub-load forecasting. Moreover, this approach utilizes the Adaptive Moment Estimation (Adam) algorithm for effective parameter optimization, enabling complex training and improving the accuracy of sub-load forecasting. We conducted evaluations of thermal load disaggregation and forecasting across a range of realistic building scenarios. The findings indicate that the integrated approach predicts sub-loads with a MAE that is up to 34.9% lower than that of the two-step approach. Additionally, it identifies occupants presence with an 18.5% higher F-score. This demonstrates its enhanced suitability for accurately predicting sub-loads and estimating future occupancy schedules.

This article is licensed under a Creative Commons Attribution 4.0 International License.

97172

Non-intrusive thermal load disaggregation and forecasting for effective HVAC systems

Elsevier Ltd

English

2024-08-01

Applied Energy

367

123379

https://doi.org/10.1016/j.apenergy.2024.123379

https://hdl.handle.net/11094/97172