Prediction of Energy Consumption of Group Buildings Based on BP-LSTM Neural Networks

DOI

10.2991/978-94-6463-308-5_20How to use a DOI?

Keywords

energy consumption prediction; BP neural networks; LSTM neural networks; group building; building planning

Abstract

Aiming at the problem that it is difficult to collect the variables that affect the prediction of building energy consumption, this paper proposes a BP-LSTM neural networks prediction model based on the combination of natural factors and human factors. First, the three basic natural factors of sunshine time, temperature, and precipitation are used to predict BP neural networks. Then according to the corresponding time, LSTM neural networks are used to predict, and a BP-LSTM combined building energy consumption prediction model is established. Taking the data of the past ten years in South China as an example, the sequential combined prediction model has higher precision and wider applicability, thus providing an effective treatment method for group architectural planning.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xiaotong Yan
PY  - 2023
DA  - 2023/12/11
TI  - Prediction of Energy Consumption of Group Buildings Based on BP-LSTM Neural Networks
BT  - Proceedings of the 2023 8th International Conference on Engineering Management (ICEM 2023)
PB  - Atlantis Press
SP  - 188
EP  - 195
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-308-5_20
DO  - 10.2991/978-94-6463-308-5_20
ID  - Yan2023
ER  -