A deep learning framework for building energy consumption forecast

摘要

This paper presents kCNN-LSTM, a deep learning framework that operates on the energy consumption data recorded at predefined intervals to provide accurate building energy consumption forecasts. kCNN-LSTM employs (ⅰ) k-means clustering - to perform cluster analysis to understand the energy consumption pattern/trend, (ⅱ) convolutional neural networks (CNN) - to extract complex features with non-linear interactions that affect energy consumption, and (ⅲ) long short term memory (LSTM) neural networks - to handle long-term dependencies through modeling temporal information in the time series data. The efficiency and applicability of kCNN-LSTM were demonstrated using a real time building energy consumption data acquired from a four--storeyed building in IIT Bombay, India. The performance of kCNN-LSTM was compared with the k-means variant of the state-of-the-art energy demand forecast models in terms of well-known quality metrics. It is also observed that the accurate energy demand forecast provided by kCNN-LSTM due to its ability to learn the spatio-temporal dependencies in the energy consumption data makes it a suitable deep learning model for energy consumption forecast problems.