Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm
Introduction
DHS (District heating system) enables utilization of waste or low-cost heat, which is the main precondition for DHS (district heating system) competitiveness when compared to onsite, individual, boilers [1]. Another precondition is high heat density. Although some previous studies showed that sparse DHSs (systems with low heat density) can be economic [2], [3], [4], [5], [6], their success highly correlates with energy taxation, which is country specific.
A possible solution for further increase of competitiveness of DHSs lies in the improvement of current control strategy [7], [8], [9]. Predictive heat load models of all consumers in the system, or at least the most influential ones, are indispensable as inputs for advanced model predictive control. Predictive models should provide several-hour-ahead forecasts of required heat, where the prediction horizon can be defined according to the endmost consumer in the network. Oktay et al. [10] developed a novel approach to predict the outdoor temperature fluctuations during daytime as a dimensionless temperature variation coefficient to simplify building energy calculations. Papakostas et al. [11] proposed a new procedure for the calculation of equivalent full-load hours of operation for heating and cooling systems, from hourly temperature bin data. Equivalent full hours used for a rough estimation of annual heating and cooling energy requirements in buildings. Korolijaa et al. [12] discussed the building characteristics needed to be considered for energy performance simulation, their values, and how they can be used in parametric studies. Seo et al. [13] developed a nine-node-based Lagrangian finite element model for estimating the heating and cooling demand of a residential building with a different envelope design, which can be useful for an architect or a construction manager in the early design phase. There have been many researches related to prediction using different algorithms [14], [15], [16], [17], [18], [19], [20], [21], [22], [23]. Utilizing the best possible methods using different sources of energies was focus of many researchers in the past [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [46], [47].
In this article, the heat load prediction models of consumers for different prediction horizons using the data acquired from one heating substation in DHS Novi Sad, Serbia is investigated. The proposed models were developed using a soft computing approach, namely SVMs (Support Vector Machines) FFA (Firefly Algorithm), which was used for optimal parameter selection.
SVMs (Support Vector Machines) were applied in different engineering disciplines [34], [35]. The prediction accuracy of an SVM model relies heavily on proper determination of model parameters [36], [37], [38]. Grid search [39] and gradient decent [40], [41] are among many searching algorithms for SVM parameters. Computational complexity is the major disadvantage of these algorithms, which restricted applicability to simple cases. Also convergence to local minima is another disadvantage of these methods. In the most cases the multiple local minima occurs and therefore evolutionary algorithms present goo approach because of their global solution to such problems.
In this study, short term predictive models of consumers' heat load were developed using the SVM method, whereas the parameters were selected using the Firefly algorithm. Results indicate that the proposed models can adequately predict the heat load for different prediction horizons. SVM-FFA results were also compared with the results of GP (genetic programming), ANNs (artificial neural networks), and SVMs with grid search algorithm.
The rest of this paper is structured as follows: materials and methods are presented in Section 2. Section 3, results and discussions are discussed. Finally, conclusion is drawn in Section 4.
Section snippets
System and data description
For model building data from DHS Novi Sad was used. Heat production in DHS Novi Sad is performed in six heat supply units (heat only boilers) and one CHP (combined head and power) unit “Novi Sad”. Heat from CHP is transferred to one main manifold where it is diverted towards the three supply units inside the station [42]. Data was collected during the 2009/10 heating season in one of the system's heating substations. Following five variables were continually measured and logged at
Results and discussion
Predictive models of heat load were developed for 1, 2, 3, 4, 5, 8, and 10 h ahead. Five models, for a prediction horizon from 1 to 5 h ahead, are intended for control purpose, while the models for 8 and 10 h ahead are intended for planning purpose.
Conclusion
Predictive models for consumers' heat load in DHSs were developed using the SVM-FFA method. The proposed SVM-FFA models were obtained with the combination of the two methods, SVM and FFA.
Data collected in heating substations in a DHS during the 2009/10 heating season were used for model building and testing. Seven predictive models for different prediction horizons (from one hour to ten hours ahead) were created using the SVM-FFA method. A comparison of SVM-FFA method with GP, ANNs, and SVMs
Acknowledgement
This project was supported by the High Impact Research Grant (UM.C/625/1/HIR/MOHE/FCSIT/15) and Fundamental Research Grant Scheme (FRGS) - FP071-2015A from the University of Malaya and the Ministry of Higher Education, Malaysia.
References (47)
- et al.
Possibilities and consequences of deregulation of the European electricity market for connection of heat sparse areas to district heating systems
Appl Energy
(2010) - et al.
Sparse district- heating in Sweden
Appl Energy
(2008) - et al.
Supply water temperature regulation problems in district heating network with both direct and indirect connection
Energy Build
(1998) - et al.
Achieving low return temperatures from district heating substations
Appl Energy
(2014) - et al.
A new approach for predicting cooling degree-hours and energy requirements in buildings
Energy
(2011) - et al.
Equivalent full-load hours for estimating heating and cooling energy requirements in buildings: Greece case study
Appl Energy
(2009) - et al.
Lagrangian finite element model for estimating the heating and cooling demand of a residential building with a different envelope design
Appl Energy
(2015) - et al.
A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation
Renew Sustain Energy Rev
(2015) - et al.
An analysis of wind energy potential and economic evaluation in Zahedan, Iran
Renew Sustain Energy Rev
(2014) - et al.
Using different methods for comprehensive study of wind turbine utilization in Zarrineh, Iran
Energy Convers Manag
(2013)
Economic feasibility of developing wind turbines in Aligoodarz, Iran
Energy Convers Manag
Assessment of solar and wind energy potentials for three free economic and industrial zones of Iran
Energy
Evaluation of wind energy potential as a power generation source for electricity production in Binalood, Iran
Renew Energy
Economic evaluation for cooling and ventilation of medicine storage warehouses utilizing wind catchers
Renew Sustain Energy Rev
Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data
Comput Electron Agric
Supervised machine learning algorithms for protein structure classification
Comput Biol Chem
A PSO and pattern search based memetic algorithm for SVMs parameters optimization
Neurocomputing
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Evolutionary tuning of SVM parameter values in multiclass problems
Neurocomputing
District heating and cooling, Lund
Profitability of sparse district heating
Appl Energy
Department of economic and social affairs
Cited by (107)
Surrogate modeling for long-term and high-resolution prediction of building thermal load with a metric-optimized KNN algorithm
2023, Energy and Built EnvironmentCitation Excerpt :The main disadvantage of ANNs is that the algorithm requires a huge number of training data, while the shortage of recorded data directly lowers the accuracy of prediction. Al-Shammari et al. [22] developed a short-term prediction model for heating loads of a district heating system using SVM. In this study, obtained test results of the SVM model showed better prediction precision than the ANN model [22].
Optimal management and data-based predictive control of district heating systems: The Novate Milanese experimental case-study
2023, Control Engineering PracticeHybrid machine learning models for predicting short-term wave energy flux
2022, Ocean Engineering