Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator

Lotta Kannari, Jussi Kiljander, Kalevi Piira, Jouko Piippo and Pekka Koponen
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Lotta Kannari: VTT Technical Research Centre of Finland, 02044 VTT Espoo, Finland
Jussi Kiljander: VTT Technical Research Centre of Finland, 02044 VTT Espoo, Finland
Kalevi Piira: VTT Technical Research Centre of Finland, 02044 VTT Espoo, Finland
Jouko Piippo: VTT Technical Research Centre of Finland, 02044 VTT Espoo, Finland
Pekka Koponen: VTT Technical Research Centre of Finland, 02044 VTT Espoo, Finland

Forecasting, 2021, vol. 3, issue 2, 1-13

Abstract: Accurate short-term forecasts of building energy consumption are necessary for profitable demand response. Short-term forecasting methods can be roughly classified into physics-based modelling and data-based modelling. Both of these approaches have their advantages and disadvantages and it would be therefore ideal to combine them. This paper proposes a novel approach that allows us to combine the best parts of physics-based modelling and machine learning while avoiding many of their drawbacks. A key idea in the approach is to provide a variety of building parameters as input for an Artificial Neural Network (ANN) and train the model with data from a large group of simulated buildings. The hypothesis is that this forces the ANN model to learn the underlying simulation model-based physics, and thus enables the ANN model to be used in place of the simulator. The advantages of this type of model is the combination of robustness and accuracy from a high-detail physics-based model with the inference speed, ease of deployment, and support for gradient based optimization provided by the ANN model. To evaluate the approach, an ANN model was developed and trained with simulated data from 900–11,700 buildings, including equal distribution of office buildings, apartment buildings, and detached houses. The performance of the ANN model was evaluated with a test set consisting of 60 buildings (20 buildings for each category). The normalized root mean square errors (NRMSE) were on average 0.050, 0.026, 0.052 for apartment buildings, office buildings, and detached houses, respectively. The results show that the model was able to approximate the simulator with good accuracy also outside of the training data distribution and generalize to new buildings in new geographical locations without any building specific heat demand data.

Keywords: building energy modelling; machine learning; artificial neural networks; demand response; short-term forecasting; simulation (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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