A generally applicable, simple and adaptive forecasting method for the short-term heat load of consumers

Nigitz, Thomas; Gölles, Markus

doi:10.1016/j.apenergy.2019.03.012

Cited by 54 publications

(15 citation statements)

References 18 publications

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“…In contrast, physical models are better in terms of generalization [39,40]. Data-driven models have widely been used for heat load forecasting using regression [35,[41][42][43][44][45], and artificial neural networks [38]. Physical models could use the consumption behaviors [46,47] or customer social behavior [36], as well as daily or seasonal patterns [48].…”

Section: Methodsmentioning

confidence: 99%

Effect of Heat Demand on Integration of Urban Large-Scale Renewable Schemes—Case of Helsinki City (60 °N)

Arabzadeh

Lund

2020

Energies

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Heat demand dominates the final energy use in northern cities. This study examines how changes in heat demand may affect solutions for zero-emission energy systems, energy system flexibility with variable renewable electricity production, and the use of existing energy systems for deep decarbonization. Helsinki city (60 °N) in the year 2050 is used as a case for the analysis. The future district heating demand is estimated considering activity-driven factors such as population increase, raising the ambient temperature, and building energy efficiency improvements. The effect of the heat demand on energy system transition is investigated through two scenarios. The BIO-GAS scenario employs emission-free gas technologies, bio-boilers and heat pumps. The WIND scenario is based on large-scale wind power with power-to-heat conversion, heat pumps, and bio-boilers. The BIO-GAS scenario combined with a low heat demand profile (−12% from 2018 level) yields 16% lower yearly costs compared to a business-as-usual higher heat demand. In the WIND-scenario, improving the lower heat demand in 2050 could save the annual system 6–13% in terms of cost, depending on the scale of wind power.

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Section: Methodsmentioning

confidence: 99%

Effect of Heat Demand on Integration of Urban Large-Scale Renewable Schemes—Case of Helsinki City (60 °N)

Arabzadeh

Lund

2020

Energies

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“…e.g. [33,38]. Using these historic point forecasts and historic measurements, residuals can be computed for which a stochastic residual model is fitted.…”

Section: Scenario Generation and Reductionmentioning

confidence: 99%

“…The scenario generation and reduction was performed as explained in Sect. 2.3.1 and the forecast method from [33] was used for obtaining the point-forecasts for the heat demand and the method from [38] for the point-forecasts of the solar thermal yield. In both cases using 60 days of historical measurements.…”

Section: Small District Heating Networkmentioning

confidence: 99%

Progressive hedging for stochastic energy management systems

et al. 2020

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Energy systems have increased in complexity in the past years due to the everincreasing integration of intermittent renewable energy sources such as solar thermal or wind power. Modern energy systems comprise different energy domains such as electrical power, heating and cooling which renders their control even more challenging. Employing supervisory controllers, so-called energy management systems (EMSs), can help to handle this complexity and to ensure the energy-efficient and cost-efficient operation of the energy system. One promising approach are

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“…Spoladore et al [8] analyzed data of heat demand for town-level aggregation and developed a model of hourly gas consumption for heating purposes. Nigitz et al [9] proposed a model, where changes in consumer behavior are covered by continuous adaptation by using historical data for the ambient temperature and the heat load. Mosavi et al [10] and Bourdeau et al [11] gave an overview of data-driven methods that can be applied to heat load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Heat Production in Combined Heat and Power Plants Using Generalized Additive Models

Bujalski

Madejski

2021

Energies

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The paper presents a developed methodology of short-term forecasting for heat production in combined heat and power (CHP) plants using a big data-driven model. An accurate prediction of an hourly heat load in the day-ahead horizon allows a better planning and optimization of energy and heat production by cogeneration units. The method of training and testing the predictive model with the use of generalized additive model (GAM) was developed and presented. The weather data as an input variables of the model were discussed to show the impact of weather conditions on the quality of predicted heat load. The new approach focuses on an application of the moving window with the learning data set from the last several days in order to adaptively train the model. The influence of the training window size on the accuracy of forecasts was evaluated. Different versions of the model, depending on the set of input variables and GAM parameters were compared. The results presented in the paper were obtained using a data coming from the real district heating system of a European city. The accuracy of the methods during the different periods of heating season was performed by comparing heat demand forecasts with actual values, coming from a measuring system located in the case study CHP plant. As a result, a model with an averaged percentage error for the analyzed period (November–March) of less than 7% was obtained.

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A generally applicable, simple and adaptive forecasting method for the short-term heat load of consumers

Cited by 54 publications

References 18 publications

Effect of Heat Demand on Integration of Urban Large-Scale Renewable Schemes—Case of Helsinki City (60 °N)

Effect of Heat Demand on Integration of Urban Large-Scale Renewable Schemes—Case of Helsinki City (60 °N)

Progressive hedging for stochastic energy management systems

Forecasting of Heat Production in Combined Heat and Power Plants Using Generalized Additive Models

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