District heating demand short-term forecasting

Petrichenko, Roman; Baltputnis, Kārlis; Sauhats, Antans Sauļus; Sobolevsky, Dimitry

doi:10.1109/eeeic.2017.7977633

Cited by 17 publications

(19 citation statements)

References 4 publications

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“…Results of thermal demand estimation using a curve fitting technique based on neural net fitting were compared with the results from time series estimation, in existing literature, based on estimation errors. Errors in the estimated value using the neural net fitting tool for winter season ranged between −23 and 31 whereas, in [22] it ranged from −33 to 15 (with 10% less range) with use of time series ANN. Further, the MAPE for 24 h ahead forecast according to [23] was 7.28% for winter which is similar to 7.3% during winter using neural net fitting as discussed here.…”

Section: Thermal Demand Estimationmentioning

confidence: 96%

“…In [21], the forecasting method is based on time series neural network with temperature and thermal consumption at a particular hour, day and previous history are taken into consideration. One month data from a DH network in Riga has been analysed for forecasting in [22] with the comparison between methods using an artificial neural network, polynomial regression model and the combination of both. With these methods, forecasts are performed by updating the statistics of actual load and temperature of the previous measurement.…”

Section: Introductionmentioning

confidence: 99%

“…This information is useful while training the curve fitting tool to estimate thermal demand. With reference to [21][22][23], thermal demand estimation is based on the past and its current state for winter. A simple, yet effective curve fitting technique for estimating the thermal demand in the residential area, based on dependent parameters such as time factor (based on consumption profile) and environment variables (apparent temperature), has been investigated and compared with actual data as well as results from existing literature.…”

Section: Introductionmentioning

confidence: 99%

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Flexibility from Electric Boiler and Thermal Storage for Multi Energy System Interaction

et al. 2019

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Active use of heat accumulators in the thermal system has the potential for achieving flexibility in district heating with the power to heat (P2H) units, such as electric boilers (EB) and heat pumps. Thermal storage tanks can decouple demand and generation, enhancing accommodation of sustainable energy sources such as solar and wind. The overview of flexibility, using EB and storage, supported by investigating the nature of thermal demand in a Danish residential area, is presented in this paper. Based on the analysis, curve-fitting tools, such as neural net and similar day method, are trained to estimate the residential thermal demand. Utilizing the estimated demand and hourly market spot price of electricity, the operation of the EB is scheduled for storing and fulfilling demand and minimizing energy cost simultaneously. This demonstrates flexibility and controlling the EB integrated into a multi-energy system framework. Results show that the curve fitting tool is effectively suitable to acknowledge thermal demands of residential area based on the environmental factor as well as user behaviour. The thermal storage has the capability of operating as a flexible load to support P2H system as well as minimize the effect of estimation error in fulfilling actual thermal demand simultaneously.

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Section: Thermal Demand Estimationmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Flexibility from Electric Boiler and Thermal Storage for Multi Energy System Interaction

et al. 2019

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“…Note, that the problem of forecasting processes is relevant in solving many problems related to optimizing power systems. The extensive scientific literature is devoted to this problem [41][42][43][44][45][46][47][48]. The approach we use is only one of the possible approaches.…”

Section: Implementation Of the First Taskmentioning

confidence: 99%

Estimating the Economic Impacts of Net Metering Schemes for Residential PV Systems with Profiling of Power Demand, Generation, and Market Prices

et al. 2018

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This article analyses the influence of supporting scheme variants on the profitability of a projected investment of residential photovoltaic systems. The focus of the paper lies in evaluating the feasibility for the power system of solar power generation technologies to achieve a balance between energy generation and support costs in a more efficient way. The case study is based on a year-long time series of examples with an hourly resolution of electricity prices from the Nord Pool power market, in addition to the power demand and solar generation of Latvian prosumers. Electric energy generation and the consumption of big data from more than 100 clients were collected. Based on these data, we predict the processes for the next 25 years, and we estimate economic indicators using a detailed description of the net metering billing system and the Monte-Carlo method. A recommendation to change the current net system to a superior one, taking into account the market cost of energy, concludes the paper.

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“…Several ML-based forecasting models were also implemented and compared for varying prediction horizons up to 24h. Several ML tools for dayahead forecasting of heat consumption of thermal loads in DHS network in the city of Riga, Latvia are implemented in [14]. The forecast results are used as inputs for decisions in day-ahead operation planning and whole electricity market participation.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Forecasting of Heat Demand of Buildings for Efficient and Optimal Energy Management Based on Integrated Machine Learning Models

Eseye

Lehtonen

2020

IEEE Trans. Ind. Inf.

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The increasing growth in the energy demand calls for robust actions to design and optimize energy-related assets for efficient and economic energy supply and demand within a smart grid setup. This paper proposes a novel integrated machine learning (ML) technique to forecast the heat demand of buildings in a District Heating System (DHS). The proposed short-term (24h-ahead) heat demand forecasting model is based the integration of Empirical Mode Decomposition (EMD), Imperialistic Competitive Algorithm (ICA) and Support Vector Machine (SVM). The proposed model also embeds a ML-based feature selection technique combining binary genetic algorithm (BGA) and Gaussian Process Regression (GPR) to obtain the most important and nonredundant variables that can constitute the input predictor subset to the forecasting model. The model is developed using a two-year (2015-2016) hourly dataset of actual district heat demand obtained from various buildings in the Otaniemi area of Espoo, Finland. Several variables from different domains such as seasonality (calendar), weather, occupancy and heat demand are used to construct the initial feature space for feature selection process. Short-term forecasting models are also implemented using the Persistence approach as a reference and other eight ML approaches: artificial neural network (ANN), genetic algorithm combined with ANN (GA-ANN), ICA-ANN, SVM, GA-SVM, ICA-SVM, EMD-GA-ANN, and EMD-ICA-ANN. The performance of the proposed EMD-ICA-SVM-based forecasting model is tested using an out-of-sample one-year (2017) hourly dataset of district heat consumption of various building types. Comparative analysis of the forecasting performance of the models was performed. The obtained results demonstrate that the devised model forecasts the heat demand with improved performance evaluated using various accuracy metrics. Moreover, the devised model achieves outperformed forecasting accuracy enhancement, compared to the other nine evaluated models.

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District heating demand short-term forecasting

Cited by 17 publications

References 4 publications

Flexibility from Electric Boiler and Thermal Storage for Multi Energy System Interaction

Flexibility from Electric Boiler and Thermal Storage for Multi Energy System Interaction

Estimating the Economic Impacts of Net Metering Schemes for Residential PV Systems with Profiling of Power Demand, Generation, and Market Prices

Short-Term Forecasting of Heat Demand of Buildings for Efficient and Optimal Energy Management Based on Integrated Machine Learning Models

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