Izvorni znanstveni članak
https://doi.org/10.13044/j.sdewes.d9.0388
Gaining Insights into Dwelling Characteristics Using Machine Learning for Policy Making on Nearly Zero-Energy Buildings with the Use of Smart Meter and Weather Data
Teo Čurčić
; Faculty of Technology, Innovation and Society, The Hague University of Applied Sciences, The Hague, The Netherlands
Rajeev Robin Kalloe
; Faculty of Technology, Innovation and Society, The Hague University of Applied Sciences, The Hague, The Netherlands
Merel Alexandra Kreszner
; Faculty of Engineering & ICT Avans University, Breda, The Netherlands
Olivier van Luijk
; Faculty of Technology, Innovation and Society, The Hague University of Applied Sciences, The Hague, The Netherlands
Santiago Puertas Puchol
; Escuela Politécnica Superior University Francisco de Vitoria, Madrid, Spain
Emilio Caba Batuecas
; Escuela Politécnica Superior University Francisco de Vitoria, Madrid, Spain
Tadeo Baldiri Salcedo Rahola
; Faculty of Technology, Innovation and Society, The Hague University of Applied Sciences, The Hague, The Netherlands
Sažetak
Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.
Ključne riječi
Smart meter; Net-Zero Energy Building; Supervised machine learning; Classification; LSTM
Hrčak ID:
274927
URI
Datum izdavanja:
31.3.2022.
Posjeta: 812 *