A study of energy conserving building design to predict heating and cooling loads by advanced data mining approach

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Author

  • Pham Anh Duc; Le Thi Kim Oanh; Ho Thi Kieu Oanh

Từ khóa:

cooling load
heating load
energy performance
energy-efficient building
swarm intelligence
data mining

Tóm tắt

Advanced data mining (DM) approaches are potential tools for solving civil engineering problems. This study investigates the potential use of advanced approaches and proposes a hybrid meta-heuristic optimization algorithm - based prediction model that integrates artificial firefly colony algorithm and machine learning prediction model. The proposed model was constructed using 768 experimental datasets from the literature with 8 input parameters and 2 output parameters including heating load (HL) and cooling load (CL). Compared to previous works, the proposed model obtained at least 33.8% to 86.9% lower error rates respectively, for CL and HL prediction. This study confirms the efficiency, effectiveness, and accuracy of the proposed approach when predicting CL and HL in building design stage. Therefore, the analytical results certainly support the feasibility of using the proposed techniques to facilitate early designs of energy conserving buildings.

Tài liệu tham khảo

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Dec 31, 2014
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Pham Anh Duc; Le Thi Kim Oanh; Ho Thi Kieu Oanh. “A Study of Energy Conserving Building Design to Predict Heating and Cooling Loads by Advanced Data Mining Approach”. Tạp Chí Khoa học Và Công nghệ - Đại học Đà Nẵng, vol 12, số p.h 85.1, Tháng Chạp 2014, tr 1-5, https://jst-ud.vn/jst-ud/article/view/1694.

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