Building Energy Consumption Prediction Using Neural-Based Models
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Keywords:Machine learning, Artificial neural networks, Building energy prediction, Six sigma
AbstractIn the recent years digital transformation became one of the most used approaches in building energy consumption optimization. Increased interest in improving energy sustainability and comfort inside buildings has created an opportunity for digital transformation to build predictive tools for energy consumption. By retrofitting or implementing new construction technologies nowadays the quantity and quality of the operational data collected has reached unprecedented levels. This data must be consumed by implementing powerful predictive tools that will provide the needed level of certainty. Adopting Six Sigma's Define, Measure, Analyze, Improve, Control (DMAIC) cycle as predictive analytics framework will make this paper accessible for both professionals working in energy industry and researchers that are developing models, creating the premises for reducing the gap between research and real-world business, guiding the use of data. Moreover, the selected strategy for preprocessing and hyperparameter selection is presented, the final selected models showing scalability and flexibility. At the end the architectures, performance and training time are discussed and then coupled with the thought process providing a way to weigh up the options. Building energy consumption prediction, it is a relevant and actual topic. Firstly, on European level, meeting the targets set by the new European Green Deal for buildings sector is relying heavily on digitization and therefore on predictive analytics. Secondly, on Romania level, the liberalization of the Energy market created an unpreceded energy price increase. The negative social impact might be diminished not only by the price reduction, but also by understanding how the energy is consumed.
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How to Cite
Buturache, A.-N., & Stancu, S. (2022). Building Energy Consumption Prediction Using Neural-Based Models. International Journal of Energy Economics and Policy, 12(2), 30–38. https://doi.org/10.32479/ijeep.12739