TY - GEN
T1 - Energy Load Forecast in Green Buildings Based on LSTM Deep Learning Model
AU - Qiang, Guofeng
AU - Tang, Shu
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Accurately predicting electrical load is crucial for effective energy management in green buildings (GB). However, the task of forecasting electricity consumption is inherently challenging due to the dynamic nature of indoor environmental changes. This study addresses this issue by employing a Long Short-Term Memory (LSTM) deep learning model to predict energy load in green buildings. By utilizing a month’s historical data encompassing temperature, humidity, and energy consumption, the LSTM model is trained to forecast energy load. The results demonstrated the effectiveness of the LSTM model in predicting energy load, with impressive performance metrics of R2 Score of 0.992 when forecasting energy load for a week. This research contributes to the field of energy management in green buildings by providing a reliable and efficient method for predicting electrical load, ultimately aiding in integrating GB into smart grids.
AB - Accurately predicting electrical load is crucial for effective energy management in green buildings (GB). However, the task of forecasting electricity consumption is inherently challenging due to the dynamic nature of indoor environmental changes. This study addresses this issue by employing a Long Short-Term Memory (LSTM) deep learning model to predict energy load in green buildings. By utilizing a month’s historical data encompassing temperature, humidity, and energy consumption, the LSTM model is trained to forecast energy load. The results demonstrated the effectiveness of the LSTM model in predicting energy load, with impressive performance metrics of R2 Score of 0.992 when forecasting energy load for a week. This research contributes to the field of energy management in green buildings by providing a reliable and efficient method for predicting electrical load, ultimately aiding in integrating GB into smart grids.
KW - Deep Learning
KW - Energy Load Forecast
KW - Green Buildings
KW - LSTM
KW - Time-series Prediction
UR - http://www.scopus.com/inward/record.url?scp=105000239559&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-1965-8_37
DO - 10.1007/978-981-96-1965-8_37
M3 - Conference Proceeding
AN - SCOPUS:105000239559
SN - 9789819619641
T3 - Lecture Notes in Electrical Engineering
SP - 408
EP - 413
BT - Proceedings of 2024 International Conference on Smart Electrical Grid and Renewable Energy, SEGRE 2024 - Volume 2
A2 - Wen, Fushuan
A2 - Liu, Haoming
A2 - Wen, Huiqing
A2 - Wang, Shunli
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Smart Electrical Grid and Renewable Energy, SEGRE 2024
Y2 - 9 August 2024 through 12 August 2024
ER -
