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

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 290-302
Author(s):  
Lotta Kannari ◽  
Jussi Kiljander ◽  
Kalevi Piira ◽  
Jouko Piippo ◽  
Pekka Koponen
Keyword(s):  
Machine Learning ◽  
Demand Forecasting ◽  
Office Buildings ◽  
Ann Model ◽  
Short Term ◽  
Equal Distribution ◽  
Advantages And Disadvantages ◽  
Apartment Buildings ◽  
Heat Demand ◽  
Mean Square Errors

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.

scholarly journals Short-term water demand forecasting using hybrid supervised and unsupervised machine learning model

Smart Water ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Mo’tamad Bata ◽  
Rupp Carriveau ◽  
David S.-K. Ting
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Demand Forecasting ◽  
Hybrid Models ◽  
Supervised Machine Learning ◽  
Forecasting Model ◽  
Short Term ◽  
Unsupervised Machine Learning ◽  
Water Demand Forecasting ◽  
Clustering Model

Abstract Regression Tree (RT) forecasting models are widely used in short-term demand forecasting. Likewise, Self-Organizing Maps (SOM) models are known for their ability to cluster and organize unlabeled big data. Herein, a combination of these two Machine Learning (ML) techniques is proposed and compared to a standalone RT and a Seasonal Autoregressive Integrated Moving Average (SARIMA) models, in forecasting the short-term water demand of a municipality. The inclusion of the Unsupervised Machine Learning clustering model has resulted in a significant improvement in the performance of the Supervised Machine Learning forecasting model. The results show that using the output of the SOM clustering model as an input for the RT forecasting model can, on average, double the accuracy of water demand forecasting. The Mean Absolute Percentage Error (MAPE) and the Normalized Root Mean Squared Error (NRMSE) were calculated for the proposed models forecasting 1 h, 8 h, 24 h, and 7 days ahead. The results show that the hybrid models outperformed the standalone RT model, and the broadly used SARIMA model. On average, hybrid models achieved double accuracy in all 4 forecast periodicities. The increase in forecasting accuracy afforded by this hybridized modeling approach is encouraging. In our application, it shows promises for more efficient energy and water management at the water utilities.

scholarly journals Predictive Optimization of the Heat Demand in Buildings at the City Level

2019 ◽  
Vol 9 (10) ◽  
pp. 1994
Author(s):  
Petri Hietaharju ◽  
Mika Ruusunen ◽  
Kauko Leiviskä ◽  
Marko Paavola
Keyword(s):  
Energy Efficiency ◽  
District Heating ◽  
Short Term ◽  
Indoor Temperature ◽  
Improve Energy Efficiency ◽  
Apartment Buildings ◽  
Heat Demand ◽  
Demand Models ◽  
The City ◽  
Modelling Approach

Easily adaptable indoor temperature and heat demand models were applied in the predictive optimization of the heat demand at the city level to improve energy efficiency in heating. Real measured district heating data from 201 large buildings, including apartment buildings, schools and commercial, public, and office buildings, was utilized. Indoor temperature and heat demand of all 201 individual buildings were modelled and the models were applied in the optimization utilizing two different optimization strategies. Results demonstrate that the applied modelling approach enables the utilization of buildings as short-term heat storages in the optimization of the heat demand leading to significant improvements in energy efficiency both at the city level and in individual buildings.

scholarly journals Short-term electricity grid maximum demand forecasting with the ARIMAX-SVR Machine Learning Hybrid Model

2021 ◽  
Vol 28 (1) ◽  
pp. 22-30
Author(s):  
Hon Fung Chow
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Hybrid Model ◽  
Regression Models ◽  
Demand Forecasting ◽  
Learning Model ◽  
Support Vector ◽  
Linear Regression Models ◽  
Short Term ◽  
Machine Learning Model

This paper proposes and discusses the viability of a short-term grid maximum demand forecasting model combining autoregressive integrated moving average with regressors (ARIMAX) and support vector regression (SVR). Grid demand forecasting is essential to generation unit scheduling, maintenance planning and system security. Traditionally, grid demand is forecasted using multivariate linear regression models with parameters adjusted to past data. A disadvantage of the linear regression model is that the parameters require regular adjustment, otherwise the prediction accuracy will deteriorate over time. With recent advances in the field of machine learning and lower computational costs, the usage of machine learning in the power industry becomes increasingly practicable. The proposed model is a machine learning model that combines ARIMAX and SVR to exploit their respective effectiveness in predicting linear and non-linear data. In contrast to linear regression models, the machine learning model automatically updates itself when new data is included. The hybrid model is benchmarked against other forecasting models and demonstrated a marked improvement in accuracy, achieving RMSE of 67.7MW and MAPE of 1.32% in a seven-day forecast.

scholarly journals Short-term water demand predictions coupling an Artificial Neural Network model and a Genetic Algorithm

2021 ◽  
Author(s):  
Majid Gholami Shirkoohi ◽  
Mouna Doghri ◽  
Sophie Duchesne
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Water Demand ◽  
Urban Water ◽  
Ann Model ◽  
Short Term ◽  
Urban Water Demand ◽  
Artificial Neural ◽  
Mean Square Errors

Abstract The application of artificial neural network (ANN) models for short-term (15 min) urban water demand predictions is evaluated. Optimization of the ANN model's hyperparameters with a Genetic Algorithm (GA) and use of a growing window approach for training the model are also evaluated. The results are compared to those of commonly used time series models, namely the Autoregressive Integrated Moving Average (ARIMA) model and a pattern-based model. The evaluations are based on data sets from two Canadian cities, providing 15 minute water consumption records over respectively 5 years and 23 months, with a respective mean water demand of 14,560 and 887 m3/d. The GA optimized ANN model performed better than the other models, with Nash-Sutcliffe Efficiencies of 0.91 and 0.83, and Relative Root Mean Square Errors of 6 and 16% for City 1 and City 2, respectively. The results of this study indicate that the optimization of the hyperparameters of an ANN model can lead to better 15 min urban water demand predictions, which are useful for many real time control applications, such as dynamic pressure control.

scholarly journals Short-term water demand forecasting using machine learning techniques

2018 ◽  
Vol 20 (6) ◽  
pp. 1343-1366 ◽  
Author(s):  
A. Antunes ◽  
A. Andrade-Campos ◽  
A. Sardinha-Lourenço ◽  
M. S. Oliveira
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Demand Forecasting ◽  
Maximum Level ◽  
Water Utilities ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Water Demand Forecasting ◽  
Learning Techniques

Abstract Nowadays, a large number of water utilities still manage their operation on the instant water demand of the network, meaning that the use of the equipment is conditioned by the immediate water necessity. The water reservoirs of the networks are filled using pumps that start working when the water level reaches a specified minimum, stopping when it reaches a maximum level. Shifting the focus to water management based on future demand allows use of the equipment when energy is cheaper, taking advantage of the electricity tariff in action, thus bringing significant financial savings over time. Short-term water demand forecasting is a crucial step to support decision making regarding the equipment operation management. For this purpose, forecasting methodologies are analyzed and implemented. Several machine learning methods, such as neural networks, random forests, support vector machines and k-nearest neighbors, are evaluated using real data from two Portuguese water utilities. Moreover, the influence of factors such as weather, seasonality, amount of data used in training and forecast window is also analysed. A weighted parallel strategy that gathers the advantages of the different machine learning techniques is suggested. The results are validated and compared with those achieved by autoregressive integrated moving average (ARIMA) also using benchmarks.

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