Heat load prediction in district heating systems with adaptive neuro-fuzzy method

Shamshirband, Shahaboddin; Petković, Dalibor; Enayatifar, Rasul; Abdullah, Abdul Hanan; Marković, Dušan; Lee, Malrey; Ahmad, Rodina

doi:10.1016/j.rser.2015.04.020

Cited by 69 publications

(22 citation statements)

References 47 publications

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“…By closely matching supply and demand, the network will run at greater efficiency, leading to lower operating temperatures, lower distribution and transmission costs, which will in turn lead to lower end user costs, and therefore more users connecting to the network, further reducing costs [115][116][117][118][119][120][121][122][123][124]. To meet demand, DHN operators can control fluid flow in the network, differential pressure and supply temperature [118]. In many cases, the operator will slightly increase the supply temperature prior to an expected demand spike, slowly increasing heat and storing it for a few hours in the network.…”

Section: Load Predictionmentioning

confidence: 99%

District Heating Challenges for the UK

Millar

Burnside

2019

Energies

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District heating uptake has grown with the increasing need for cleaner and more efficient energy supply. This has resulted in a rising number of new developments signing up to a district heating scheme, typically powered by Combined Heat and Power (CHP) boilers or biomass boilers with supplemental electrical or gas grid connections. These schemes have advanced rapidly in recent years, with much of the research focus targeting lower carbon technologies, improved load prediction and peak demand management. We assess the current status of District Heating Networks (DHNs) in the United Kingdom using published case studies and suggest next steps to improvement. Our findings show that the United Kingdom has good potential for uptake of district energy given the current political climate and government incentives, however significant improvements must be made to further penetrate the heating market.

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Section: Load Predictionmentioning

confidence: 99%

District Heating Challenges for the UK

Millar

Burnside

2019

Energies

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“…[3] ARMA outdoor temperature, heat load, behavior of the consumers [11] PSO-SVR outdoor temperature, supply water temperature, supply water pressure, circular flow, heat load [12] SVM-FFA time lagged heat load, outdoor temperature, primary return temperatures [14] SVR, PLS, RT forward temperature, return temperature, flow rate, heat load [15] ELM outdoor temperature, primary supply temperature, primary return temperature, flow on primary side [18] ANFIS outdoor temperature, primary supply temperature, primary return temperature, secondary supply temperature, secondary return temperature, flow on primary side…”

Section: Algorithm Influencing Factorsmentioning

confidence: 99%

District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model

et al. 2019

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The smart district heating system (SDHS) is an important element of the construction of smart cities in Northern China; it plays a significant role in meeting heating requirements and green energy saving in winter. Various Internet of Things (IoT) sensors and wireless transmission technologies are applied to monitor data in real-time and to form a historical database. The accurate prediction of heating loads based on massive historical datasets is the necessary condition and key basis for formulating an optimal heating control strategy in the SDHS, which contributes to the reduction in the consumption of energy and the improvement in the energy dispatching efficiency and accuracy. In order to achieve the high prediction accuracy of SDHS and to improve the representation ability of multi-time-scale features, a novel short-term heating load prediction algorithm based on a feature fusion long short-term memory (LSTM) model (FFLSTM) is proposed. Three characteristics, namely proximity, periodicity, and trend, are found after analyzing the heating load data from the aspect of the hourly time dimension. In order to comprehensively utilize the data’s intrinsic characteristics, three LSTM models are employed to make separate predictions, and, then, the prediction results based on internal features and other external features at the corresponding moments are imported into the high-level LSTM model for fusion processing, which brings a more accurate prediction result of the heating load. Detailed comparisons between the proposed FFLSTM algorithm and the-state-of-art algorithms are conducted in this paper. The experimental results show that the proposed FFLSTM algorithm outperforms others and can obtain a higher prediction accuracy. Furthermore, the impact of selecting different parameters of the FFLSTM model is also studied thoroughly.

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“…The authors propose a BN to predict the total consumer water heat consumption in households. Shamshirband et al [21] construct an adaptive neurofuzzy inference system (ANFIS), which is a special case of the ANN family, to predict heat load for individual consumers in a DH system. Their result indicates that more improvements of the model are required for prediction horizons greater than 1 hour.…”

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

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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.

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Heat load prediction in district heating systems with adaptive neuro-fuzzy method

Cited by 69 publications

References 47 publications

District Heating Challenges for the UK

District Heating Challenges for the UK

District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model

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

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