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

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  • 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
    Centro de Microcomputación y Sistemas Distribuidos (CEMISID), University of The Andes, Mérida 5101, Venezuela
    Grupo de Investigación, Desarrollo e Innovación en Tecnologías de Informacion y Comunicación (GIDITIC), EAFIT University, Medellín 50022, Colombia)

  • Maria D. R-Moreno

    (Escuela Politécnica Superior, University of Alcalá, 28805 Alcalá de Henares, Spain
    Intelligent Autonomous Systems Group (IAS), TNO, 2597 AK The Hague, The Netherlands)

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.

Suggested Citation

  • Daniela Durand & Jose Aguilar & Maria D. R-Moreno, 2022. "An Analysis of the Energy Consumption Forecasting Problem in Smart Buildings Using LSTM," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13358-:d:944723
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    References listed on IDEAS

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    1. Sarah Hadri & Mehdi Najib & Mohamed Bakhouya & Youssef Fakhri & Mohamed El Arroussi, 2021. "Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings," Energies, MDPI, vol. 14(18), pages 1-17, September.
    2. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    3. Dhowmya Bhatt & Danalakshmi D & A. Hariharasudan & Marcin Lis & Marlena Grabowska, 2021. "Forecasting of Energy Demands for Smart Home Applications," Energies, MDPI, vol. 14(4), pages 1-19, February.
    4. Anam-Nawaz Khan & Naeem Iqbal & Atif Rizwan & Rashid Ahmad & Do-Hyeun Kim, 2021. "An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings," Energies, MDPI, vol. 14(11), pages 1-25, May.
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    2. Syauqi, Ahmad & Uwitonze, Hosanna & Chaniago, Yus Donald & Lim, Hankwon, 2024. "Design and optimization of an onboard boil-off gas re-liquefaction process under different weather-related scenarios with machine learning predictions," Energy, Elsevier, vol. 293(C).

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