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.
Highlights
The energy systems increasingly need demand response for balancing and reduction of environmental impacts such as greenhouse gas emissions
The machine learning (ML) model does not have to learn to approximate the underlying physics encoded into the simulation models, and the model is still not able to perform well in situations not covered by the training data. To tackle these limitations of the existing work, this paper proposes a novel approach for combining physics-based simulator and ML based modelling
To evaluate the proposed approach, we developed a Feed Forward Neural Network (FFNN) to forecast buildings heat demand based on building characteristics, weather, and temporal data
Summary
The energy systems increasingly need demand response for balancing and reduction of environmental impacts such as greenhouse gas emissions. Buildings have excellent demand response potential [1,2,3] Tapping this potential requires the ability to accurately forecast short-term energy demand, its flexibility, and the load control responses. Accurate modeling and forecasting are essential to utilize a model-based optimal control for demand response and peak demand shaving. Detailed physical models have high accuracy, but are difficult to utilize in an on-line building operation because they have many parameters and require large computation time and power. Statistics and machine learning (ML) models are fast, and their accuracy good enough for model-based control, but these methods require a large amount of training data that covers the building operation range. Reducing the computational cost and memory demand for building energy modeling and optimal control, while maintaining the accuracy, is an urgent issue for on-line practical applications
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