9 papers analyzed
These studies suggest that predictive forecasting using various machine learning models, such as Random Forests, sensor-based models, Bayesian bidirectional LSTM, and hybrid methods, improves energy efficiency and sustainability in buildings and smart grid systems.
Predictive forecasting in energy consumption is a critical tool for enhancing energy efficiency. By accurately predicting energy usage patterns, stakeholders can optimize energy management, reduce waste, and improve sustainability. This synthesis explores how predictive forecasting contributes to energy efficiency based on insights from multiple research papers.
Machine Learning Models for Accurate Predictions:
Granularity and Data Utilization:
Smart Grid and Smart Home Applications:
Privacy and Communication Efficiency:
Predictive forecasting significantly enhances energy efficiency by leveraging advanced machine learning models to accurately predict energy consumption patterns. The granularity of data and integration with other variables further improve forecast accuracy. Applications in smart grids and smart homes demonstrate the practical benefits of these models, while privacy-preserving techniques like federated learning ensure secure and efficient implementation. Overall, predictive forecasting is a vital tool for optimizing energy management and promoting sustainability.
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