Short-term heat load forecasting in district heating systems using artificial neural networks

Benalcazar, Pablo; Kamiński, Jacek

doi:10.1088/1755-1315/214/1/012023

Cited by 7 publications

(7 citation statements)

References 15 publications

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“…Accurate prediction of heat loads has become an interesting field of application for modern time series forecasting methods. Its importance even increases with a rising global energy demand, decreasing reserves of fossil fuels and the impact of using fossil fuels on climate change (Benalcazar and Kamiński, 2019). District heating and cooling can be a sufficient way to reduce carbon dioxide emissions by optimizing fuel consumption (Werner, 2017).…”

Section: Related Workmentioning

confidence: 99%

“…This paper therefore aims to give a first estimation of what results can be expected in this area. Most of the work that was done on the topic of energy demand forecasting focused on a 24 hour time horizon (Benalcazar and Kamiński, 2019;Xue et al, 2019). This paper instead focuses on an extended forecasting period of up to 72 hours, in order to allow further optimised dispatch planning of power plants and thermal energy storages.…”

Section: Related Workmentioning

confidence: 99%

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Temporal Fusion Transformer for thermal load prediction in district heating and cooling networks

Behrens¹,

Leiprecht²,

Brantl³

et al. 2022

Linköping Electronic Conference Proceedings

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Accurate forecasting of thermal loads is a critical factor for operating district heating and cooling networks economically, efficiently and with minimized emissions. If thermal loads are known with high accuracy in advance, use of renewable energies can be maximized, and fossil generation, in particular in peaking units, can be avoided. Machine learning has already proven to be an efficient tool for time series forecasting in this context. One recent advancement in machine learning is the "Temporal Fusion Transformer" (TFT), which shows especially good results in the area of time series forecasting. This paper examines the performance of TFT in the concrete context of thermal load forecasting for district heating and cooling networks. First, a brief summary of differences between TFT and other machine learning methods is given. Secondly, it is described how the method can be adopted to train a machine learning model for thermal load forecasting. The data to train and evaluate the neural network is based on 8 years of hourly operating data made available from the district heating network of the city of Ulm in Germany. The presented technique is used to produce 72 hours of heating load forecasts for three different district heating grids in the city of Ulm. The results are compared to forecasts of other machine learning methods that have been previously made as part of the publicly funded research project "deepDHC", in order to evaluate if TFT is an improvement to further reduce forecasting uncertainties.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Temporal Fusion Transformer for thermal load prediction in district heating and cooling networks

Behrens¹,

Leiprecht²,

Brantl³

et al. 2022

Linköping Electronic Conference Proceedings

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show abstract

“…Recently, a model using the artificial neural network (ANN), e.g. [7], is proposed to address the nonlinearity present heat consumption/load data. However, the training of ANN is often challenging, as it has the tendency of being trapped in local optima [8].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model

Chatterjee¹,

Bayer²,

Maier³

2021

Preprint

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In combating climate change, an effective demand-based energy supply operation of the district energy system (DES) for heating or cooling is indispensable. As a consequence, an accurate forecast of heat consumption on the consumer side poses an important first step towards an optimal energy supply. However, due to the non-linearity and non-stationarity of heat consumption data, the prediction of the thermal energy demand of DES remains challenging. In this work, we propose a forecasting framework for thermal energy consumption within a district heating system (DHS) based on kernel Support Vector Regression (kSVR) using real-world smart meter data. Particle Swarm Optimization (PSO) is employed to find the optimal hyper-parameter for the kSVR model which leads to the superiority of the proposed methods when compared to a state-of-the-art ARIMA model. The average MAPE is reduced to 2.07% and 2.64% for the individual meter-specific forecasting and for forecasting of societal consumption, respectively.Preprint. Under review.

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“…Social factors were taken into consideration in the prediction of total energy demand in several cases such as Spain [35], China [36], and Turkey [37,38]. The application of social components alongside meteorological and past consumption data was also studied in district heating networks [39,40]. In all relevant studies, the results showed that the inclusion of social parameters in the modelling can increase the model's overall accuracy [41,42].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks for Natural Gas Consumption Forecasting

2020

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The present research study explores three types of neural network approaches for forecasting natural gas consumption in fifteen cities throughout Greece; a simple perceptron artificial neural network (ANN), a state-of-the-art Long Short-Term Memory (LSTM), and the proposed deep neural network (DNN). In this research paper, a DNN implementation is proposed where variables related to social aspects are introduced as inputs. These qualitative factors along with a deeper, more complex architecture are utilized for improving the forecasting ability of the proposed approach. A comparative analysis is conducted between the proposed DNN, the simple ANN, and the advantageous LSTM, with the results offering a deeper understanding the characteristics of Greek cities and the habitual patterns of their residents. The proposed implementation shows efficacy on forecasting daily values of energy consumption for up to four years. For the evaluation of the proposed approach, a real-life dataset for natural gas prediction was used. A detailed discussion is provided on the performance of the implemented approaches, the ANN and the LSTM, that are characterized as particularly accurate and effective in the literature, and the proposed DNN with the inclusion of the qualitative variables that govern human behavior, which outperforms them.

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Short-term heat load forecasting in district heating systems using artificial neural networks

Cited by 7 publications

References 15 publications

Temporal Fusion Transformer for thermal load prediction in district heating and cooling networks

Temporal Fusion Transformer for thermal load prediction in district heating and cooling networks

Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model

Application of Artificial Neural Networks for Natural Gas Consumption Forecasting

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