An Analysis of the Energy Consumption Forecasting Problem in Smart Buildings Using LSTM

Daniela Durand, Jose Aguilar () and Maria D. R-Moreno
Additional contact information
Daniela Durand: Escuela Politécnica Superior, University of Alcalá, 28805 Alcalá de Henares, Spain
Jose Aguilar: Escuela Politécnica Superior, University of Alcalá, 28805 Alcalá de Henares, Spain
Maria D. R-Moreno: Escuela Politécnica Superior, University of Alcalá, 28805 Alcalá de Henares, Spain

Sustainability, 2022, vol. 14, issue 20, 1-22

Abstract: This work explores the process of predicting energy consumption in smart buildings based on the consumption of devices and appliances. Particularly, this work studies the process of data analysis and generation of prediction models of energy consumption in Smart Buildings. Specifically, this article defines a feature engineering approach to analyze the energy consumption variables of buildings. Thus, it presents a detailed analysis of the process to build prediction models based on time series, using real energy consumption data. According to this approach, the relationships between variables are analyzed, thanks to techniques such as Pearson and Spearman correlations and Multiple Linear Regression models. From the results obtained with these, an extraction of characteristics is carried out with the Principal Component Analysis (PCA) technique. On the other hand, the relationship of each variable with itself over time is analyzed, with techniques such as autocorrelation (simple and partial), and Autoregressive Integrated Moving Average (ARIMA) models, which help to determine the time window to generate prediction models. Finally, prediction models are generated using the Long Short-Term Memory (LSTM) neural network technique, taking into account that we are working with time series. This technique is useful for generating predictive models due to its architecture and long-term memory, which allow it to handle time series very well. The generation of prediction models is organized into three groups, differentiated by the variables that are considered as descriptors in each of them. Evaluation metrics, RMSE, MAPE, and R 2 are used. Finally, the results of LSTM are compared with other techniques in different datasets.

Keywords: forecasting models; energy consumption; smart buildings; machine learning; time series; LSTM technique (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/20/13358/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/20/13358/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:20:p:13358-:d:944723

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().