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A deep learning framework for building energy consumption forecast

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  • Somu, Nivethitha
  • Raman M R, Gauthama
  • Ramamritham, Krithi

Abstract

Increasing global building energy demand, with the related economic and environmental impact, upsurges the need for the design of reliable energy demand forecast models. This work 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.

Suggested Citation

  • Somu, Nivethitha & Raman M R, Gauthama & Ramamritham, Krithi, 2021. "A deep learning framework for building energy consumption forecast," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    • Handle: RePEc:eee:rensus:v:137:y:2021:i:c:s1364032120308753
    DOI: 10.1016/j.rser.2020.110591
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