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Smart energy strategies
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Orientador(es)
Resumo(s)
Accurately predicting and optimizing heating and cooling demands in building energy management is crucial for enhancing efficiency and reducing energy consumption. Traditional methods often struggle with building energy usage patterns' nonlinear and variable nature. With the advent of advanced data collection through smart sensors, there is a growing need for intelligent systems to leverage this data to provide actionable insights. This study addresses the gap by developing a recommendation system using three machine and deep learning models, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and XGBoost to predict and optimize the efficiency levels of space heating, ceiling cooling, and free cooling systems. Our proposed solution harnesses the power of these models, with LSTM performing best overall, to forecast energy consumption across hourly and daily timescales, enabling precise adjustments and efficient energy management. The methodology involves extensive data preprocessing, including hierarchical imputation of missing values and label encoding of categorical variables, followed by the transformation of raw data into efficiency levels. The deep learning model architecture, consisting of sequential layers, captures long-term dependencies in the data, while grid search-based hyperparameter tuning optimizes model performance. Results indicate high predictive accuracy, with R-squared values demonstrating the model's ability to explain up to 97.2 % of the variance in hourly space heating, 95.2 % in daily ceiling cooling, and 93 % in daily free cooling energy consumption. Additionally, we interpret graphs using OpenAI's GPT-4 model to enhance understanding and facilitate actionable insights. This interpretation enhances the clarity of the predictive results, supporting more informed decision-making in energy management. The significance of this work lies in its potential to transform energy management practices in building environments, providing a robust tool for optimizing heating and cooling operations and contributing to overall energy efficiency.
Descrição
Almeida, F., Castelli, M., Côrte-Real, N., Fallarino, C., & Manzoni, L. (2025). Smart energy strategies: Leveraging LSTM and LLMs for advanced energy management. Energy and AI, 22, Article 100642. https://doi.org/10.1016/j.egyai.2025.100642 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. This work was funded by the European Union throught the project 101084013 - DIGITAL4Business. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HADEA). Neither the European Union nor the granting authority can be held responsible for them.
Palavras-chave
Energy prediction LSTM HVAC optimization Smart buildings Deep learning Engineering (miscellaneous) General Energy Artificial Intelligence SDG 9 - Industry, Innovation, and Infrastructure SDG 7 - Affordable and Clean Energy