Applied machine learning: Forecasting heat load in district heating system

Idowu, Samuel Ο.; Saguna, Saguna; Åhlund, Christer; Schelén, Olov

doi:10.1016/j.enbuild.2016.09.068

Cited by 202 publications

(86 citation statements)

References 16 publications

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“…LR is an approach for modelling the relationship between a scalar dependent variable y and one or multiple explanatory variables denoted X. It is often used as a baseline model for the evaluation of machine learning methods, and we continue this practice analogous to the related studies [8] and [20]. We shall restrict attention to multiple linear regression (MLR) [21], and model the thermal load P at time t by a linear equation.…”

Section: Linear Regressionmentioning

confidence: 99%

Operational thermal load forecasting in district heating networks using machine learning and expert advice

Geysen

Somer

Johansson

et al. 2018

Energy and Buildings

115

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Forecasting thermal load is a key component for the majority of optimization solutions for controlling district heating and cooling systems. Recent studies have analysed the results of a number of data-driven methods applied to thermal load forecasting, this paper presents the results of combining a collection of these individual methods in an expert system. The expert system will combine multiple thermal load forecasts in a way that it always tracks the best expert in the system. This solution is tested and validated using a thermal load dataset of 27 months obtained from 10 residential buildings located in Rottne, Sweden together with outdoor temperature information received from a weather forecast service. The expert system is composed of the following data-driven methods: linear regression, extremely randomized trees regression, feed-forward neural network and support vector machine. The results of the proposed solution are compared with the results of the individual methods.

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Section: Linear Regressionmentioning

confidence: 99%

Operational thermal load forecasting in district heating networks using machine learning and expert advice

Geysen

Somer

Johansson

et al. 2018

Energy and Buildings

115

View full text Add to dashboard Cite

show abstract

“…Secondly, the integration of K-means and one-versus-one support vector machine is applied to extract the behavior characteristics of SETS to obtain the work time t. Then the electricity consumption of SETS is obtained by integrating average power consumption. The performance of the prediction is tested by the error calculation Equation (17). The error levels are divided according to the size of the errors.…”

Section: Cyber-physical Approachmentioning

confidence: 99%

“…Among the existing methods, machine learning (neural network) is commonly used in heat load prediction. A data-driven approach with machine learning is presented to predict the heat load in the rooms [17], and a bi-directional long short-term memory recurrent neural network is proposed to combine the correlation between past information and future information to predict the thermal storage time in [18]. A linear regression model with the ambient temperature is proposed to predict heat load in [19].…”

mentioning

confidence: 99%

Electricity Consumption Prediction of Solid Electric Thermal Storage with a Cyber–Physical Approach

Yang

Wang

et al. 2019

Energies

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This paper proposes a cyber-physical approach to enhance the prediction accuracy of electricity consumption of solid electric thermal storage (SETS) system, which integrates a physical model and a data-based cyber model. In the cyber-physical model, the prediction error of the physical model is used as an input of the cyber model to further calibrate the prediction error. Firstly, customers' behavior characteristics are extracted by the integration of K-means and one-versus-one support vector machine. Secondly, based on the behavior characteristics and ambient temperature, the physical model is developed to predict daily electricity consumption. Finally, the error levels of physical model are classified, together with the temperature and prediction values of the physical model, are selected as the inputs of the cyber model using the back propagation (BP) neural network to calibrate the results of the physical model. The effectiveness of the proposed cyber-physical model (CPM) is verified by a 1 MW SETS system. The simulation results show that, compared with the physical model (PM) and cyber model (CM), the maximum relative errors (MRE) with the CPM are reduced to 25.4% and 4.8%, respectively.Energies 2019, 12, 4744 2 of 18 forced-air electric furnaces is built to predict the thermal energy storage, which is an early application of SETS [5]. This work does not take into account the effect of continuously changing the ambient temperature on thermal energy storage. A PM is integrated into the TRNSYS calculation tool to evaluate the optimal thermal energy storage of forced-air electric furnaces with changing ambient temperature [6]. Therefore, it is necessary to improve the basis of the existing methods, and enhance the prediction accuracy. However, the customers' behavior characteristics of SETS are not considered in the above-mentioned PMs, which is very important to improve the accuracy of prediction.In [7], a sparse continuous conditional random fields method was proposed to predict electric load with the identification of behavior. The data from advanced metering infrastructure is used to understand the power consumption patterns to improve the load forecasting accuracy in [8].The prediction accuracy would be significantly enhanced with the consideration of behavior. However, the working mode of SETS are completely different from those of conventional electric loads [9]. SETS is charged by the off-peak electricity, and its thermal energy is released all-day. During the off-peak hours, usually from 21:00 to 6:00, the heating elements quickly heat the dense bricks to a high temperature owning to its cheaper electricity prices. During the peak period from 6:00 to 21:00, the heating elements are switched off, and SETS continues to release its thermal energy to warm the rooms. Many behavior characteristics of SETS directly affect the heat load demand, such as all-day continuous work (e.g., convenience store), and holiday and non-holiday period (e.g., star hotel), which need to be considered. The conventional models o...

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“…Machine learning is the core of artificial intelligence, which leads to the wide application in the field of artificial intelligence because it makes the computer intelligent. Machine learning such as artificial neural networks and support vector machine is often used for load forecasting [19,20], which can greatly improve the prediction accuracy.…”

Section: Development Trendmentioning

confidence: 99%

Recent Trends in Load Forecasting Technology for the Operation Optimization of Distributed Energy System

Tian

Yan

et al. 2017

Energies

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Abstract:The introduction of renewable resources into the distributed energy system has challenged the operation optimization of the distributed energy system. Integration of new technologies and diversified characteristics on the demand side has exerted a great influence on the distributed energy system. In this paper, by way of literature review, first, the topological structure and the mathematical expression of the distributed energy system were summarized, and the trend of enrichment and diversification and the new characteristics of the system were evaluated. Then, the load forecasting technology was reviewed and analyzed from two aspects, fundamental research and application research. Research methods of the distributed energy system under the new trend of energies were discussed, and the boundaries of the broadened distributed energy technology were explored.

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Applied machine learning: Forecasting heat load in district heating system

Cited by 202 publications

References 16 publications

Operational thermal load forecasting in district heating networks using machine learning and expert advice

Operational thermal load forecasting in district heating networks using machine learning and expert advice

Electricity Consumption Prediction of Solid Electric Thermal Storage with a Cyber–Physical Approach

Recent Trends in Load Forecasting Technology for the Operation Optimization of Distributed Energy System

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