District Heating System Load Prediction Using Machine Learning Method

We present our data-driven supervised machine-learning (ML) model to predict heat load for buildings in a district heating system (DHS). Even though ML has been used as an approach to heat load prediction in literature, it is hard to select an approach that will qualify as a solution for our case as existing solutions are quite problem specific. For that reason, we compared and evaluated three ML algorithms within a framework on operational data from a DH system in order to generate the required prediction model. The algorithms examined are Support Vector Regression (SVR), Partial Least Square (PLS), and random forest (RF). We use the data collected from buildings at several locations for a period of 29 weeks. Concerning the accuracy of predicting the heat load, we evaluate the performance of the proposed algorithms using mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient. In order to determine which algorithm had the best accuracy, we conducted performance comparison among these ML algorithms. The comparison of the algorithms indicates that, for DH heat load prediction, SVR method presented in this paper is the most efficient one out of the three also compared to other methods found in the literature.

Download Full-text

Heat demand forecasting in District Heating Network using XGBoost algorithm

E3S Web of Conferences ◽

10.1051/e3sconf/202132300004 ◽

2021 ◽

Vol 323 ◽

pp. 00004

Author(s):

Maciej Bujalski ◽

Paweł Madejski ◽

Krzysztof Fuzowski

Keyword(s):

Machine Learning ◽

District Heating ◽

Ambient Air ◽

Measuring System ◽

Machine Learning Method ◽

Learning Method ◽

Demand Model ◽

District Heating System ◽

Heat Demand ◽

Extreme Gradient Boosting

Forecasting an hourly heat demand during different periods of district heating network operation is essential to optimize heat production in the CHP plant. The paper presents the heat demand model in the real district heating system with a peak load of 200 MW. The predictive model was developed with the use of the machine learning method based on the historical data. The XGBoost (Extreme Gradient Boosting) algorithm was applied to find the relation between actual heat demand and predictors such as weather data and behavioral parameters like an hour of the day, day of week, and month. The method of model training and evaluating was discussed. The results were assessed by comparing hourly heat demand forecasts with actual values from a measuring system located in the CHP plant. The RMSE and MAPE error for the analysed time period were calculated and then benchmarked with an exponential regression model supplied with ambient air temperature. It was found that the machine learning method allows to obtain more accurate results due to the incorporation of additional predictors. The MAPE and RMSE for the XGBoost model in the day-ahead horizon were 6.9% and 8.7MW, respectively.

Download Full-text

Applied machine learning: Forecasting heat load in district heating system

Energy and Buildings ◽

10.1016/j.enbuild.2016.09.068 ◽

2016 ◽

Vol 133 ◽

pp. 478-488 ◽

Cited By ~ 69

Author(s):

Samuel Idowu ◽

Saguna Saguna ◽

Christer Åhlund ◽

Olov Schelén

Keyword(s):

Machine Learning ◽

Heat Load ◽

District Heating ◽

Heating System ◽

District Heating System ◽

Applied Machine Learning

Download Full-text

Heat load prediction of small district heating system using artificial neural networks

Thermal Science ◽

10.2298/tsci16s5355s ◽

2016 ◽

Vol 20 (suppl. 5) ◽

pp. 1355-1365 ◽

Cited By ~ 5

Author(s):

Milos Simonovic ◽

Vlastimir Nikolic ◽

Emina Petrovic ◽

Ivan Ciric

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

District Heating ◽

Heating System ◽

Load Prediction ◽

District Heating System ◽

Artificial Neural

Accurate models for heat load prediction are essential to the operation and planning of a utility company. Load prediction helps a heat utility to make important and advanced decisions in district heating systems. As a popular data driven method, artificial neural networks are often used for prediction. The main idea is to achieve quality prediction for a short period in order to reduce the consumption of heat energy production and increased coefficient of exploitation of equipment. To improve the short term prediction accuracy, this paper presents a kind of improved artificial neural network model for 1 to 7 days ahead prediction of heat consumption of energy produced in small district heating system. Historical data set of one small district heating system from city of Nis, Serbia, was used. Particle swarm optimization is applied to adjust artificial neural network weights and threshold values. In this paper, application of feed forward artificial neural network for short-term prediction for period of 1, 3, and 7 days, of small district heating system, is presented. Two test data sets were considered with different interruption non-stationary performances. Comparison of prediction accuracy between regular and improved artificial neural network model was done. The comparison results reveal that improved artificial neural network model have better accuracy than that of artificial neural network ones.

Download Full-text

A Forecasting Method of District Heat Load Based on Improved Wavelet Neural Network

Journal of Energy Resources Technology ◽

10.1115/1.4047020 ◽

2020 ◽

Vol 142 (10) ◽

Author(s):

Zhongbin Zhang ◽

Ye Liu ◽

Lihua Cao ◽

Heyong Si

Keyword(s):

Neural Networks ◽

Energy Conservation ◽

Heat Load ◽

Prediction Models ◽

District Heating ◽

Heating System ◽

Load Prediction ◽

Urban District ◽

District Heating System ◽

Forecasting Method

Abstract Energy conservation of urban district heating system is an important part of social energy conservation. In response to the situation that the setting of heat load in the system is unreasonable, the heat load forecasting method is adopted to optimize the allocation of resources. At present, the artificial neural networks (ANNs) are generally used to forecast district heat load. In order to solve the problem that networks convergence is slow or even not converged due to the random initial parameters in traditional wavelet neural networks (WNNs), the genetic algorithm with fast convergence ability is used to optimize the network structure and initial parameters of heat load prediction models. The results show that when the improved WNN is applied to forecast district heat load, the prediction error is as low as 2.93%, and the accuracy of prediction results is improved significantly. At the same time, the stability and generalization ability of the prediction model are improved.

Download Full-text