Potential Analysis of the Attention-Based LSTM Model in Ultra-Short-Term Forecasting of Building HVAC Energy Consumption

Abstract

Predicting system energy consumption accurately and adjusting dynamic operating parameters of the HVAC system in advance is the basis of realizing the model predictive control (MPC). In recent years, the LSTM network had made remarkable achievements in the field of load forecasting. This paper aimed to evaluate the potential of using an attentional-based LSTM network (A-LSTM) to predict HVAC energy consumption in practical applications. To evaluate the application potential of the A-LSTM model in real cases, the training set and test set used in experiments are the real energy consumption data collected by Kitakyushu Science Research Park in Japan. Pearce analysis was first carried out on the source data set and built the target database. Then five baseline models (A-LSTM, LSTM, RNN, DNN, and SVR) were built. Besides, to optimize the super parameters of the model, the Tree-structured of Parzen Estimators (TPE) algorithm was introduced. Finally, the applications are performed on the target database, and the results are analyzed from multiple perspectives, including model comparisons on different sizes of the training set, model comparisons on different system operation modes, graphical examination, etc. The results showed that the performance of the A-LSTM model was better than other baseline models, it could provide accurate and reliable hourly forecasting for HVAC energy consumption.

Review of Energy-Related Machine Learning Applications in Drying Processes

“…Among these, LSTM is probably the most popular. It is an improved RNN model that is able to solve the problem of gradient disappearance or explosion, which often occurs when training continuous data [58]. In ref.…”

Prediction of Heat Energy Consumption by LSTM Sequence-to-Sequence Models

“…LSTM model was used with attention mechanism to provide forecasting for solar assisted water heating systems and a comparison of the numbers of layers and neurons were made [3]. Another paper discussed LSTM with attention mechanism to create a system that forecasting the heating, ventilation, and air conditioning systems and compared it to different models [4]. A different paper has addressed the GRU architecture combined with CNN, and made a comparison of the performance between GRU and CNN separately, for instance, combining them together to forecast Electricity of Wuwei and Gansu provinces [5].…”

Time Delay of Wide Area Damping Control in Urban Power Grid: Model-Based Analysis and Data-Driven Compensation

“…Recently, the rapid development of artificial intelligence has created the potential for time delay compensation with neural networks. Among the various neural networks, the long shortterm memory network (LSTM) is a kind of modern recurrent neural network that is designed for handling time series data (Xu et al, 2021). It has been successfully employed in power systems for islanding detection (Abdelsalam et al, 2020), load and generation forecast (Liu et al, 2020)- (Alavi et al, 2021), fast event identification (Li Z. et al, 2021), and measurements prediction (Wang et al, 2021).…”