Abstract
Scheduling forecasting activities and improving the forecasting accuracy is important to deliver energy efficiency to the customers. However, it is also important to reduce the computational effort dedicated to these forecasting activities to ensure more effective environment sustainability. This paper proposes two forecasting algorithms known as artificial neural networks and k-nearest neighbors to anticipate energy patterns of a building monitoring data from five-to-five minutes. Using a case study with an annual historic and one week test, different scenarios are defined to test the forecasting activities with both higher and lower computational effort. It is achieved to ensure energy predictions with above reasonable accuracies evaluations while decreasing the computational effort, and the respective energy consumption, dedicated to forecasting activities.
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Acknowledgements
This article is a result of the project RETINA (NORTE-01–0145-FEDER-000062), by (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). Pedro Faria is supported by FCT with grant CEECIND/01423/2021. The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team.
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Ramos, D., Faria, P., Gomes, L., Vale, Z. (2023). CPU Computation Influence on Energy Consumption Forecasting Activities of a Building. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_6
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DOI: https://doi.org/10.1007/978-3-031-18050-7_6
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