scholarly journals Heat Load Numerical Prediction for District Heating System Operational Control

2021 ◽  
Vol 58 (3) ◽  
pp. 121-136
Author(s):  
D. Rusovs ◽  
L. Jakovleva ◽  
V. Zentins ◽  
K. Baltputnis
Keyword(s):  
Ambient Temperature ◽  
Heat Load ◽  
District Heating ◽  
Heating System ◽  
Training Data ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Data Set ◽  
District Heating System ◽  
Heating Systems

Abstract To develop an advanced control of thermal energy supply for domestic heating, a number of new challenges need to be solved, such as the emerging need to plan operation in accordance with an energy market-based environment. However, to move towards this goal, it is necessary to develop forecasting tools for short- and long-term planning, taking into account data about the operation of existing heating systems. The paper considers the real operational parameters of five different heating networks in Latvia over a period of five years. The application of regression analysis for heating load dependency on ambient temperature results in the formulation of normalized slope for the regression curves of the studied systems. The value of this parameter, the normalized slope, allows describing the performance of particular heating systems. Moreover, a heat load forecasting approach is presented by an application of multiple regression methods. This short-term (day-ahead) forecasting tool is tested on data from a relatively small district heating system with an average load of 20 MW at ambient temperature of 0 °C. The deviations of the actual heat load demand from the one forecasted with various training data set sizes and polynomial orders are evaluated for two testing periods in January of 2018. Forecast accuracy is assessed by two parameters – mean absolute percentage error and normalized mean bias error.

scholarly journals Automated Statistical Methods for Fault Detection in District Heating Customer Installations

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 113 ◽  
Author(s):  
Sara Månsson ◽  
Kristin Davidsson ◽  
Patrick Lauenburg ◽  
Marcus Thern
Keyword(s):  
Energy Efficiency ◽  
Statistical Methods ◽  
District Heating ◽  
Heating System ◽  
Data Set ◽  
District Heating System ◽  
Cooling Performance ◽  
Heating Systems ◽  
Major Interest ◽  
District Heating Systems

In order to develop more sustainable district heating systems, the district heating sector is currently trying to increase the energy efficiency of these systems. One way of doing so is to identify customer installations in the systems that have poor cooling performance. This study aimed to develop an algorithm that was able to detect the poorly performing installations automatically using meter readings from the installations. The algorithm was developed using statistical methods and was tested on a data set consisting of data from 3000 installations located in a district heating system in Sweden. As many as 1273 installations were identified by the algorithm as having poor cooling performance. This clearly shows that it is of major interest to the district heating companies to identify the installations with poor cooling performance rapidly and automatically, in order to rectify them as soon as possible.

scholarly journals Technology Pathways and Economic Analysis for Transforming High Temperature to Low Temperature District Heating Systems

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3218
Author(s):  
Pedro Durán ◽  
Herena Torio ◽  
Patrik Schönfeldt ◽  
Peter Klement ◽  
Benedikt Hanke ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Renewable Energy ◽  
High Temperature ◽  
Low Temperature ◽  
Economic Analysis ◽  
District Heating ◽  
Heating System ◽  
District Heating System ◽  
Heating Systems ◽  
District Heating Systems

There are 1454 district heating systems in Germany. Most of them are fossil based and with high temperature levels, which is neither efficient nor sustainable and needs to be changed for reaching the 2050 climate goals. In this paper, we present a case study for transforming a high to low temperature district heating system which is more suitable for renewable energy supply. With the Carnot Toolbox, a dynamic model of a potential district heating system is simulated and then transformed to a low temperature supply. A sensitivity analysis is carried out to see the system performance in case space constrains restrict the transformation. Finally, an economic comparison is performed. Results show that it is technically possible to perform the transformation until a very low temperature system. The use of decentralized renewable sources, decentralized heat storage tanks and the placement of a heat pump on each building are the key points to achieve the transformation. Regarding the sensitivity analysis, the transformation is worth doing until the seasonal storage and solar collector field sizes are reduced to 60% and 80% of their values in the reference case, respectively. The economic analysis shows, however, that it is hard for highly efficient low temperature renewable based heat networks to compete with district heating systems based on a centralized fossile CHP solution. Thus, though the presented transformation is technically possible, there is a strong need to change existing economic schemes and policies for fostering a stronger promotion of renewable energy policies in the heat sector.

scholarly journals Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Fisnik Dalipi ◽  
Sule Yildirim Yayilgan ◽  
Alemayehu Gebremedhin
Keyword(s):  
Machine Learning ◽  
Heat Load ◽  
District Heating ◽  
Heating System ◽  
Partial Least Square ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
Support Vector ◽  
Load Prediction ◽  
District Heating System

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.

scholarly journals District heating simulation in the aspect of heat supply safety

2018 ◽  
Vol 45 ◽  
pp. 00005 ◽  
Author(s):  
Bożena Babiarz ◽  
Paweł Kut
Keyword(s):  
District Heating ◽  
Heating System ◽  
Point Of View ◽  
Simulation Methods ◽  
District Heating System ◽  
Heating Systems ◽  
Network Failure ◽  
State Security ◽  
Wide Range ◽  
Heating Networks

District heating systems as strategic objects from the point of view of state security must ensure reliability and security in supply of heat to their customers [1, 2]. Thanks to computer simulation methods, district heating companies can analyse the operation of the heating networks at the design and operation stage. Computer simulations also offer a wide range of possibilities in the aspect of optimization of the district heating operation as well as prediction and analysis of network failure effects [3-6]. The paper concerns the simulation of a district heating network. The methods for the simulation of heating networks were characterized and simulations of district heating system were carried out. The effects of the failure were analysed at different values of outside temperatures and for different durations of failure. The value of compensation for undelivered heat was also determined. Simulations were carried out for an actual district heating system located in Rzeszow.

Heat Accumulator in Large District Heating Systems: Simulation and Optimisation

2010 ◽  
Author(s):  
Krzysztof Badyda ◽  
Wojciech Bujalski ◽  
Jarosław Milewski ◽  
Michał Warchoł
Keyword(s):  
Heat Production ◽  
District Heating ◽  
Heating System ◽  
Heat Accumulator ◽  
District Heating System ◽  
Heating Systems ◽  
District Heating Systems ◽  
On Line ◽  
Systems Simulation

Heat accumulators in large district heating systems are used to buffer heat production. Their main purpose is to make heat production as independent as possible from district heating system demand. To do this effectively a heat accumulator of appropriate capacity must be selected. In large district heating systems, heat accumulators can be used for equalising production over periods lasting a few hours. Accumulators can be used for optimising electricity and heat production to achieve possible highest income. It may be important in situations where on-line prices change. An optimising algorithm for heat accumulator use is shown and commented. Typical working situations are simulated and results presented.

scholarly journals Excess heat utilization combined with thermal storage integration in district heating systems using renewables

2020 ◽  
Vol 24 (6 Part A) ◽  
pp. 3673-3684
Author(s):  
Borna Doracic ◽  
Marino Grozdek ◽  
Tomislav Puksec ◽  
Neven Duic
Keyword(s):  
Service Sector ◽  
Peak Load ◽  
District Heating ◽  
Thermal Storage ◽  
Heating System ◽  
District Heating System ◽  
Heating Systems ◽  
Excess Heat ◽  
Heat Demand ◽  
District Heating Systems

District heating systems already play an important role in increasing the sustainability of the heating sector and decreasing its environmental impact. However, a high share of these systems is old and inefficient and therefore needs to change towards the 4th generation district heating, which will incorporate various energy sources, including renewables and excess heat of different origins. Especially excess heat from industrial and service sector facilities is an interesting source since its potential has already been proven to be highly significant, with some researches showing that it could cover the heat demand of the entire residential and service sector in Europe. However, most analyses of its utilisation in district heating are not done on the hourly level, therefore not taking into account the variability of its availability. For that reason, the main goal of this work was to analyse the integration of industrial excess heat into the district heating system consisting of different configurations, including the zero fuel cost technologies like solar thermal. Furthermore, cogeneration units were a part of every simulated configuration, providing the link to the power sector. Excess heat was shown to decrease the operation of peak load boiler and cogeneration, that way decreasing the costs and environmental effect of the system. However, since its hourly availability differs from the heat demand, thermal storage needs to be implemented in order to increase the utilisation of this source. The analysis was performed on the hourly level in the energyPRO software

Applied machine learning: Forecasting heat load in district heating system

2016 ◽  
Vol 133 ◽  
pp. 478-488 ◽  
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

scholarly journals Techno-economic analysis of implementing thermal storage for peak load shaving in a campus district heating system with waste heat from the data centre

2021 ◽  
Vol 246 ◽  
pp. 09003
Author(s):  
Haoran Li ◽  
Juan Hou ◽  
Yuemin Ding ◽  
Natasa Nord
Keyword(s):  
Waste Heat ◽  
Peak Load ◽  
District Heating ◽  
Thermal Storage ◽  
Heating System ◽  
Water Tank ◽  
District Heating System ◽  
Heating Systems ◽  
Heat Demand ◽  
District Heating Systems

Peak load has significant impacts on the economic and environmental performance of district heating systems. Future sustainable district heating systems will integrate thermal storages and renewables to shave their peak heat demand from traditional heat sources. This article analysed the techno-economic potential of implementing thermal storage for peak load shaving, especially for the district heating systems with waste heat recovery. A campus district heating system in Norway was chosen as the case study. The system takes advantage of the waste heat from the campus data centre. Currently, about 20% of the heating bill is paid for the peak load, and a mismatch between the available waste heat and heat demand was detected. The results showed that introducing water tank thermal storage brought significant effects on peak load shaving and waste heat recovery. Those effects saved up to 112 000 EUR heating bills annually, and the heating bill paid for the peak load could be reduced by 15%. Meanwhile, with the optimal sizing and operation, the payback period of the water tank could be decreased to 13 years. Findings from this study might help the heat users to evaluate the economic feasibility of introducing thermal storage.

scholarly journals APLICATION OF REGRESSION ANALYSIS IN FEASIBILITY STUDIES OF CONSUMER CONNECTION TO DISTRICT HEATING SYSTEMS

2021 ◽  
Vol XXVIII (4) ◽  
pp. 121-132
Author(s):  
Corina Chelmenciuc ◽  
◽  
Constantin Borosan ◽  
Vadim Lisnic ◽  
◽  
...  
Keyword(s):  
Regression Analysis ◽  
Urban Areas ◽  
District Heating ◽  
Feasibility Studies ◽  
Heating System ◽  
Primary Energy ◽  
Energy Resources ◽  
District Heating System ◽  
Heating Systems ◽  
District Heating Systems

Nowadays, both globally and in Europe, and nationally, there is a tendency to promote district heating systems to the detriment of individual ones to heat dwellings in urban areas. The need to develop the DHSs is indisputable considering the topicality of global warming, the depletion of the primary energy resources and the energy efficiency trend. This article presents the method of applying regression analysis in feasibility studies for the projects of new heat consumers connection to the district heating system (hereinafter – DHS) or previously disconnected consumers reconnection via individual heating points (hereinafter – IHP) when the necessary investments are to be borne by the DHS operator, and the thermal energy is produced in cogeneration. At the same time, it is demonstrated that there is a direct and linear correlation between fuel consumption and electricity and heat produced in cogeneration at CHP plant.

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