Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm. (October 2021)

• Record Type: Journal Article
• Authors: Luo, X.J.; Oyedele, Lukumon O.
• Is Part Of: Advanced engineering informatics. Volume 50(2021)
• Publication Date: 2021-10
• Subjects: Long-short term memory -- Genetic algorithm -- Building energy consumption -- Energy forecast -- Energy management system -- Adaptive; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2021.101357
• Languages: English
• ISSNs: 1474-0346

Abstract

Graphical abstract: Highlights: Long-short term memory to capture interrelationship between energy data and time. GA to select LSTM architecture to improve forecasting accuracy and robustness. Effects of historical weather and time horizon of past information is analyzed. Real-life testing of the proposed model is conducted on two educational buildings. A powerful tool to assist net-zero building operation and mitigate climate change. Abstract: The real-world building can be regarded as a comprehensive energy engineering system; its actual energy consumption depends on complex affecting factors, including various weather data and time signature. Accurate energy consumption forecasting and effective energy system management play an essential part in improving building energy efficiency. The multi-source weather profile and energy consumption data could enable integrating data-driven models and evolutionary algorithms to achieve higher forecasting accuracy and robustness. The proposed building energy consumption forecasting system consists of three layers: data acquisition and storage layer, data pre-processing layer and data analytics layer. The core part of the data analytics layer is a hybrid genetic algorithm (GA) and long-short term memory (LSTM) neural network model for accurate and robust energy prediction. LSTM neural network is adopted to capture the interrelationship between energy consumption data and time. GA is adopted to select the optimal architecture for LSTM neural networks to improve its forecasting accuracy and robustness. The hyper-parameters for determining LSTM architecture include the number of LSTM layers, number of neurons in each LSTM layer, dropping rate of each LSTM layer and network learning rate. Meanwhile, the effects of historical weather profile and time horizon of past information are also investigated. Two real-life educational buildings are adopted to test the performance of the proposed building energy consumption forecasting system. Experiments reveal that the proposed adaptive LSTM neural network performs better than the existing feedforward neural network and LSTM-based prediction models in accuracy and robustness. It also outperforms those LSTM networks whose hyper-parameters are determined by grid search, Bayesian optimisation and PSO. Such accurate energy consumption prediction can play an essential role in various areas, including daily building energy management, decision making of facility managers, building information model designs, net-zero energy operation, climate change mitigation and circular economy.