Advancements in predictive modeling for energy management using Machine Learning

The integration of machine learning techniques in energy management has become pivotal in optimizing energy consumption and reducing operational costs. As the demand for energy efficiency grows, various studies have demonstrated the effectiveness of predictive modeling driven by advanced algorithms. Mawson and Hughes (2020) explored the use of deep neural networks to forecast energy consumption and environmental conditions in manufacturing facilities, highlighting the superior performance of feedforward and recurrent neural networks in predicting building energy needs and workshop conditions. Similarly, Walker et al. (2020) examined machine learning algorithms for predicting electricity demand at both individual building and cluster levels, finding that methods like boosted-tree, random forest, and artificial neural networks (ANNs) provided accurate hourly predictions, crucial for understanding short-term energy dynamics. On the other hand, Deng, Fannon, and Eckelman (2018) compared machine learning methods to SARIMA models for predicting energy use intensity (EUI) in U.S. commercial buildings. Their study revealed that while machine learning algorithms offered modest improvements in accuracy, SARIMA models were effective with limited data. El Alaoui et al. (2023) further highlighted the strengths of machine learning over SARIMA in predicting heating energy consumption, though SARIMA also proved useful in scenarios with minimal training data. Jana, Ghosh, and Sanyal (2020) proposed a hybrid deep learning approach combining maximal overlap discrete wavelet transformation (MODWT) with long short-term memory (LSTM) networks, showing its effectiveness in forecasting energy consumption across various sectors. Overall, these studies underscore the potential of machine learning and hybrid models in enhancing energy management strategies, improving accuracy, and optimizing energy usage across different contexts. Continued advancements in these technologies will be essential for developing effective energy solutions and achieving sustainability goals.