Analysis of machine learning models to predict heating demand and electricity consumption in buildings

Aug 3, 2026

Accurate prediction of energy consumption in buildings enables measurable energy savings and lower operating costs. This thesis evaluates the predictive performance of eight machine learning models for heating demand and electricity consumption using two case buildings in Eastern Norway. Model accuracy is assessed with four metrics: RMSE, MAE, MAPE, and R². Weather data, weekday index, and working hour index are used as input features for training and comparison. Results indicate that SVR performs best for heating demand, while QNBP gives the most accurate electricity consumption predictions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages89

Reading Time3 h 44 min

AccessFree to download

Summary

Study purpose and scope

Data and preprocessing

Machine learning models and evaluation metrics

Key results and conclusions

MASTER’S THESIS May 24, 2024 INSTITUTT FOR BYGG OG ENERGITEKNIKK | DEPTARTMENT OF BUILT ENVIRONMENTSTUDIEPROGRAM ENERGI & MILJØ I BYGG | ENERGY & ENVIRONMENT IN BUILDINGS Analysis of machine learning models to predict heating demand and electricity consumption in buildings. Jørgen Norø Sørensen Institutt for Bygg-og energiteknikk | Department of Built Environment Studieprogram Energi & miljø i bygg | Energy & environment in buildings Postadresse: Postboks 4 St. Olavs plass, 0130 Oslo Besøksadresse: Pilestredet 35, Oslo Group no. - Classification Open Access Telefon 67 23 50 00 [www. oslomet. no](www. oslomet. no) MASTER’S THESIS | Title
Analysis of machine learning models to predict heating demand and electricity consumption in buildings. | Date
May 24, 2024 | | --- | --- | | | No. of pages / appendices 60/16 | | Authors
Jørgen Norø Sørensen | Supervisors
Moon K. Kim | | Conducted for / in collaboration with
Oslomet / SINTEF | Contact persons Moon K. Kim Åse Lekang Sørensen | | --- | --- | Abstract Accurate prediction of energy consumption in buildings can lead to energy and cost savings. This research analyze how accurate 8 different machine learning models are to accurately heating demand and predict electricity consumption for 2 different case buildings in Eastern part of Norway. The accuracy of each machine learning model was analyzed based on 4 different evaluation metrics; RMSE, MAE, MAPE and R2. SVR model produced most accurate prediction on heating demand and QNBP model produced most accurate prediction electricity consumption. Weather data, weekday index and working hour index are used as input features to train machine learning models. Keywords Data analysis Machine learning models Accuracy Preface This master thesis is written by Jørgen Norø Sørensen attending the master program Energy and environment in buildings at OsloMet. Thanks to my supervisor professor Moon Keun Kim, for guidance and tips on how to produce results in this master thesis. I would also like to thank Sintef for sharing data about energy consumption in buildings, COFACTOR. Jørgen Norø Sørensen, Oslo, 24 Mai 2024 Summary Reduction of energy consumption in buildings is a desired goal for many people. This goal may be achieved if sufficient planing of building construction in early design phase is done. In this project is the accuracy of 8 different machine learning models tested. The purpose of this study was to analyze which machine learning model predict most accurate results of heating demand and electricity consumption in two case buildings. Each model was analyzed based on input variables, hyper-parameters settings, and how the accuracy of predictions was compared to previous studies. Two case buildings in eastern part of Norway is used for data analysis and testing of machine learning models. For building 1 it is provided data about hourly heating demand and for building 2 it is provided data about hourly electricity consumption. Data mining are done on both dataset before they are used to predict energy consumption of the two buildings. Machine learning models that are tested in this thesis is a total of 8 machine learning models. 4 of these models are ANN models, namely Levenberg-Marquardt back propagation, Bayesian regularization back propagation, Scaled conjugate gradient back propagation and Quasi-Newton back propagation. Therest of the machine learning models are XGBoost, Adaboost, Linear regression and Support vector regression model. ANN models are simulated in a programming software called MATLAB 2021a and the other 4 models are simulated in a programming software called Python. Accuracy of each models are analyzed based on 4 different quality measures, namely RMSE, MAE, MAPE and R2. Results show that the models struggle to adapt to data on heating demand. Predictions on electricity consumption is however more accurate. A challenge when it comes to prediction with m

FAQs

Which machine learning models achieved the best prediction performance in this thesis?

SVR produced the most accurate predictions for heating demand. QNBP produced the most accurate predictions for electricity consumption.

What input features were used to train the machine learning models?

Weather data, weekday index, and working hour index were used as input features.

How was model accuracy evaluated?

Accuracy was analyzed using four evaluation metrics: RMSE, MAE, MAPE, and R².

Aug 3, 2026

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