Hourly Daylight Illuminance Prediction Considering Seasonal and Daylight Condition-Based Meteorological Analog Intervals

Zhiyi Zhu, Xingyu Wang, Jinghan Hao, Linkun Yang and Ying Yu ()
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Zhiyi Zhu: School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
Xingyu Wang: School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
Jinghan Hao: School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
Linkun Yang: School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
Ying Yu: School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China

Sustainability, 2025, vol. 17, issue 11, 1-24

Abstract: With the growing global demand for energy optimization, particularly in the building sector, accurate daylight illuminance prediction plays a key role in enhancing energy efficiency through natural lighting and intelligent lighting systems. This study proposes a novel prediction model that integrates Meteorological Analog Intervals with a hybrid TCN-Transformer-BILSTM architecture to address the issue of insufficient prediction accuracy caused by the influence of various complex factors on daylight illuminance, as well as sudden weather changes, fluctuating meteorological conditions, and short-term variations. The model uses Grey Relational Analysis and Cosine Similarity to select historical data similar to the target moment in terms of meteorological conditions and time attributes, and constructs Meteorological Analog Intervals by combining the preceding and following time steps, providing high-quality data for the subsequent model development. The model effectively combines the multi-scale feature extraction capability of TCN, the global correlation-capturing advantage of Transformer, and the bidirectional temporal modeling characteristic of BILSTM to predict the temporal dynamics of daylight illuminance. Based on the measured data from Xi’an in 2023, experiments show that the proposed MAIL-TCN-Trans-BILSTM model achieves RMSEs of 1425.83 Lux and 2581.45 Lux under optimal and suboptimal daylight conditions, respectively, with MAPE reductions of 9–12% and 4–6% compared to baseline models. The proposed Meteorological Analog Intervals method significantly enhances the prediction accuracy and robustness of the model, especially in scenarios with complex and variable meteorological conditions, providing data support for intelligent lighting control systems.

Keywords: daylight illuminance; Meteorological Analog Intervals (MAIL); Meteorological Analog Instants (MAIT); deep learning; prediction model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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