Work

PRENERGET: A framework for the inclusion and adaptation strategies of machine learning models for energy demand forecasting in buildings

Year: 2022

Type: conference-paper

Abstract: Machine Learning (ML) models are key enablers for the implementation of different energy-efficiency strategies in buildings. There are a variety of frameworks that facilitate the development of ML models, but it is necessary to move into a different environment for their deployment and exploitation. Furthermore, their performance tends to degrade over time. Consequently, they need to be regularly evaluated and upgraded to ensure the robustness of the overall solution. The seamless exploitation, adaptation and evolution of ML models is still an open issue nowadays, and in this article, a software framework called PRENERGET aimed at addressing this issue is presented.

Source: Computing in construction

DOI: 10.35490/ec3.2022.149

Authors: Iker Esnaola-Gonzalez, Meritxell Gomez-Omella, Susana Ferreiro, Francisco Javier Diez, Alvaro García

Institution: Tekniker

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: bronze

Funders: Steadman Philippon Research Institute, European Commission, Eusko Jaurlaritza