Appraisal of soft computing methods for short term consumers' heat load prediction in district heating systems

Protić, Milan; Shamshirband, Shahaboddin; Anisi, Mohammad Hossein; Petković, Dalibor; Mitić, Dragan; Raos, Miomir; Arif, Muhammad; Alam, Khubaib Amjad

doi:10.1016/j.energy.2015.01.079

Cited by 43 publications

(10 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their results indicated that the GWA technique can predict HL of buildings more accurately than the other models (i.e., GSGP, ANN, EMARS, SVR, MLP, and RF). Similar works for predicting HL of buildings can be found in the following literatures [22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 77%

Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

et al. 2019

View full text Add to dashboard Cite

In this study, a novel technique to support smart city planning in estimating and controlling the heating load (HL) of buildings, was proposed, namely PSO-XGBoost. Accordingly, the extreme gradient boosting machine (XGBoost) was developed to estimate HL first; then, the particle swarm optimization (PSO) algorithm was applied to optimize the performance of the XGBoost model. The classical XGBoost model, support vector machine (SVM), random forest (RF), Gaussian process (GP), and classification and regression trees (CART) models were also investigated and developed to predict the HL of building systems, and compared with the proposed PSO-XGBoost model; 837 investigations of buildings were considered and analyzed with many influential factors, such as glazing area distribution (GAD), glazing area (GA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), and relative compactness (RC). Mean absolute percentage error (MAPE), root-mean-squared error (RMSE), variance account for (VAF), mean absolute error (MAE), and determination coefficient (R2), were used as the statistical criteria for evaluating the performance of the above models. The color intensity, as well as the ranking method, were also used to compare and evaluate the models. The results showed that the proposed PSO-XGBoost model was the most robust technique for estimating the HL of building systems. The remaining models (i.e., XGBoost, SVM, RF, GP, and CART) yielded more mediocre performance through RMSE, MAE, R2, VAF, and MAPE metrics. Another finding of this study also indicated that OH, RA, WA, and SA were the most critical parameters for the accuracy of the proposed PSO-XGBoost model. They should be particularly interested in smart city planning as well as the optimization of smart cities.

show abstract

Section: Introductionmentioning

confidence: 77%

Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Various methods and tools from statistics and computer science have been applied to tackle the problem of heat load forecasting for different systems, including among others: recursive least squares using simple linear models [16,17,18], multiple linear regression [19], autoregressive models with exogenous inputs [9,20], seasonal autoregressive integrated moving average [21], classification and regression tree [19], neural networks [19,22] (multiple neural networks are proposed in [23] in the different context of gas networks, a comparison of radial basis function network feedforward backpropagation networks, neuro-fuzzy interference systems and their combination is found in [24], and a more detailed review for the case of power demand is made in [25]) and neuro-fuzzy logic [26] (an adaptive neuro-fuzzy logic inference system is presented in [27] in the context of electricity demand), support vector machines [19] (combined with a discrete wavelet transform algorithm [22], or with either polynomial or radial basis function as kernel functions [28]), combination of wavelet-based multiresolution analysis and artificial neural networks [8,29], and genetic programming [22]. K-nearest neighbour and Markov-chains are additional possibilities of prediction algorithms [30].…”

Section: State Of the Artmentioning

confidence: 99%

“…Typical external input variables to the predictors are ambient (outdoor) temperature [16,18,20,22,28,33] (both daily mean and maximum were used in [24] where a daily resolution was adopted), supply temperature of the water in the district heating system [22], global sun radiation [16,18,20,24], and wind speed [16,18,20]. Humidity was also mentioned as a potential explanatory variable [19,20,24], although [20] discarded it as it was not statistically significant in the chosen model.…”

Section: State Of the Artmentioning

confidence: 99%

Online short-term forecast of greenhouse heat load using a weather forecast service

2017

View full text Add to dashboard Cite

 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

show abstract

“…Their result indicates that more improvements of the model are required for prediction horizons greater than 1 hour. Protić et al [22] study the relevance of short-term heat load prediction for operation control in DH network. Here, authors apply SVR for heat load prediction for only one substation for time horizon of every 15 minutes.…”

Section: Related Workmentioning

confidence: 99%

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

Dalipi

Yayilgan

Gebremedhin

2016

Applied Computational Intelligence and Soft Computing

View full text Add to dashboard Cite

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.

show abstract

Appraisal of soft computing methods for short term consumers' heat load prediction in district heating systems

Cited by 43 publications

References 8 publications

Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

Online short-term forecast of greenhouse heat load using a weather forecast service

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

Contact Info

Product

Resources

About