scholarly journals Predictive Optimization of the Heat Demand in Buildings at the City Level

2019 ◽  
Vol 9 (10) ◽  
pp. 1994
Author(s):  
Petri Hietaharju ◽  
Mika Ruusunen ◽  
Kauko Leiviskä ◽  
Marko Paavola
Keyword(s):  
Energy Efficiency ◽  
District Heating ◽  
Short Term ◽  
Indoor Temperature ◽  
Improve Energy Efficiency ◽  
Apartment Buildings ◽  
Heat Demand ◽  
Demand Models ◽  
The City ◽  
Modelling Approach

Easily adaptable indoor temperature and heat demand models were applied in the predictive optimization of the heat demand at the city level to improve energy efficiency in heating. Real measured district heating data from 201 large buildings, including apartment buildings, schools and commercial, public, and office buildings, was utilized. Indoor temperature and heat demand of all 201 individual buildings were modelled and the models were applied in the optimization utilizing two different optimization strategies. Results demonstrate that the applied modelling approach enables the utilization of buildings as short-term heat storages in the optimization of the heat demand leading to significant improvements in energy efficiency both at the city level and in individual buildings.

scholarly journals Enabling Demand Side Management: Heat Demand Forecasting at City Level

Materials ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 202 ◽  
Author(s):  
Petri Hietaharju ◽  
Mika Ruusunen ◽  
Kauko Leiviskä
Keyword(s):  
Energy Efficiency ◽  
Demand Forecasting ◽  
Demand Side Management ◽  
Measurement Information ◽  
Demand Side ◽  
Demand Forecast ◽  
Building Stock ◽  
Improve Energy Efficiency ◽  
Heat Demand ◽  
The City

Implementation of new energy efficiency measures for the heating and building sectors is of utmost importance. Demand side management offers means to involve individual buildings in the optimization of the heat demand at city level to improve energy efficiency. In this work, two models were applied to forecast the heat demand from individual buildings up to a city-wide area. District heating data at the city level from more than 4000 different buildings was utilized in the validation of the forecast models. Forecast simulations with the applied models and measured data showed that, during the heating season, the relative error of the city level heat demand forecast for 48 h was 4% on average. In individual buildings, the accuracy of the models varied based on the building type and heat demand pattern. The forecasting accuracy, the limited amount of measurement information and the short time required for model calibration enable the models to be applied to the whole building stock. This should enable demand side management and lead to the predictive optimization of heat demand at city level, leading to increased energy efficiency.

Thermodynamic Analysis of Minimum Extraction Ratio for Coal-Fired CHP Plants

2014 ◽  
Vol 472 ◽  
pp. 1052-1056
Author(s):  
Chun Hui Liao ◽  
Zhi Gang Zhou ◽  
Jia Ning Zhao
Keyword(s):  
Energy Efficiency ◽  
Thermodynamic Analysis ◽  
District Heating ◽  
Combined Heat And Power ◽  
Exergy Efficiency ◽  
Extraction Ratio ◽  
Dual Purpose ◽  
Heat Demand ◽  
Chp System ◽  
Minimum Heat

For evaluating the performance of combined heat and power district heating (CHP-DH) system, some thermodynamic indicators of CHP system, include energy efficiency, exergy efficiency, RPES and RAI, are introduced in this paper. Based on two condensed and heating dual purpose plants, the values of these indicators are calculated with different extraction ratio. The results show that RAI and RPES are more reasonable to be used to assess CHP-DH system and there is a minimum extraction ratio for each unit, which is 0.4 for given plants in this paper, to keep CHP-DH beneficial compared with separate heat and power (SHP) system. Besides, the minimum heat demand of CHP-DH system should be larger than the supplied heat correspond to minimum extraction ratio.

Bringing Combined Heat and Power/District Heating to Two UK Cities

1987 ◽  
Vol 201 (3) ◽  
pp. 187-192
Author(s):  
P C Warner ◽  
R A McFadden ◽  
R A J Moodie ◽  
G P White
Keyword(s):  
District Heating ◽  
Field Work ◽  
Combined Heat And Power ◽  
Private Investor ◽  
Heat Demand ◽  
The City

Edinburgh and Belfast are two of the cities where the financial prospects for district heating from combined heat and power (CHP) are being investigated by consortia combining industrial membership (substantially the same for both) with strong local interests; the object is to learn whether city CHP schemes can appeal to the private investor. The paper deals with the historical build-up of interest in CHP in both places, leading to the formation of consortia in response to a government invitation, and the award of grant-in-aid announced in January 1985. It then explains how the two studies have been planned and sets out their content: the key technical and commercial factors, and also the statutory and other more general considerations. The work is well under way, and the paper reports on progress, including field work to ascertain heat demand, the choice of fuels and sites for heat-only sources and for the combined plant, and the sequencing of implementation progressively across the city.

Development of an Operational Planning Tool for Geothermal Plants With Heat and Power Production

2020 ◽  
Vol 142 (9) ◽  
Author(s):  
Matthäus Irl ◽  
Jerry Lambert ◽  
Christoph Wieland ◽  
Hartmut Spliethoff
Keyword(s):  
District Heating ◽  
Power Production ◽  
Operational Planning ◽  
Mixed Integer ◽  
Planning Tool ◽  
Short Term ◽  
Heating Systems ◽  
Heat Demand ◽  
District Heating Systems ◽  
Geothermal Plant

Abstract A short-term operational planning tool for geothermal plants with heat and power production connected to large district heating systems is developed. The software tool contains, among other features, a heat demand forecasting model for district heating systems. Two options, such as linear regression and artificial neural networks, are compared. As the result shows, artificial neural networks with the Bayesian Regularization Backpropagation Algorithm have a high generalization capability and are suitable to forecast the heat demand of large district heating systems with high accuracy. Data from a district heating system with about 70-MW load supplied by a geothermal plant in the south of Munich (Germany) are used for comparison and assessment of all methods. After developing a suitable heat forecast, the heat and power production site is modeled by using mixed-integer linear programming. Mixed-integer linear programming has proven to be a suitable method to model the operation of geothermal plants with heat and power production as well as to solve the planning optimization problem. As the results show, the short-term operational planning tool can optimize the operation of single components as well as of the overall geothermal plant with regard to various objective functions. The tool maximizes the revenues from the sold heat and electricity minus the costs for the boiler fuel and the heat purchased from a connected adjacent geothermal plant. A retro perspective operation investigation has proven that the profitability of the considered geothermal plant could be significantly increased by using the developed software.

scholarly journals The Influence of Energy Renovation on the Change of Indoor Temperature and Energy Use

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3179 ◽  
Author(s):  
Anti Hamburg ◽  
Targo Kalamees
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Use ◽  
Primary Energy ◽  
Hot Water ◽  
Indoor Temperature ◽  
Research Issues ◽  
Apartment Buildings ◽  
Electricity Use ◽  
Heating Energy Consumption

The aim of the renovation of apartment buildings is to lower the energy consumption of those buildings, mainly the heating energy consumption. There are few analyses regarding those other energy consumptions which are also related to the primary energy need for calculating the energy efficiency class, including the primary energy need of calculated heating, domestic hot water (DHW), and household electricity. Indoor temperature is directly connected with heating energy consumption, but it is not known yet how much it will change after renovation. One of the research issues relates to the change of electricity and DHW usage after renovation and to the question of whether this change is related to the users’ behavior or to changes to technical solutions. Thirty-five renovated apartment buildings have been analyzed in this study, where the data of indoor temperature, airflow, and energy consumption for DHW with and without circulation and electricity use in apartments and common rooms has been measured. During research, it turned out that the usage of DHW without circulation and the usage of household electricity do not change after renovation. Yet there is a major increase in indoor temperature and DHW energy use in buildings that did not have circulation before the renovation. In addition, a small increase in the use of electricity in common areas was discovered. This study will offer changes in calculations for the energy efficiency number.

scholarly journals Energy Use and Perceived Indoor Environment in a Swedish Multifamily Building before and after Major Renovation

2018 ◽  
Vol 10 (3) ◽  
pp. 766 ◽  
Author(s):  
Lina La Fleur ◽  
Patrik Rohdin ◽  
Bahram Moshfegh
Keyword(s):  
Energy Efficiency ◽  
Thermal Comfort ◽  
Indoor Environment ◽  
Energy Use ◽  
Ventilation System ◽  
Heating Season ◽  
Indoor Temperature ◽  
Exhaust Ventilation ◽  
Heat Demand ◽  
Before And After

Improved energy efficiency in the building sector is a central goal in the European Union and renovation of buildings can significantly improve both energy efficiency and indoor environment. This paper studies the perception of indoor environment, modelled indoor climate and heat demand in a building before and after major renovation. The building was constructed in 1961 and renovated in 2014. Insulation of the façade and attic and new windows reduced average U-value from 0.54 to 0.29 W/m2·K. A supply and exhaust ventilation system with heat recovery replaced the old exhaust ventilation. Heat demand was reduced by 44% and maximum supplied heating power was reduced by 38.5%. An on-site questionnaire indicates that perceived thermal comfort improved after the renovation, and the predicted percentage dissatisfied is reduced from 23% to 14% during the heating season. Overall experience with indoor environment is improved. A sensitivity analysis indicates that there is a compromise between thermal comfort and energy use in relation to window solar heat gain, internal heat generation and indoor temperature set point. Higher heat gains, although reducing energy use, can cause problems with high indoor temperatures, and higher indoor temperature might increase thermal comfort during heating season but significantly increases energy use.

scholarly journals Assigning Energetic Archetypes to a Digital Cadastre and Estimating Building Heat Demand. An Example from Hamburg, Germany

2020 ◽  
Vol 24 (1) ◽  
pp. 233-253
Author(s):  
Ivan Dochev ◽  
Hannes Seller ◽  
Irene Peters
Keyword(s):  
Energy Efficiency ◽  
Model Performance ◽  
Building Stock ◽  
Heat Demand ◽  
Consumption Data ◽  
Construction Type ◽  
Building Heat ◽  
The City ◽  
Source Of Error ◽  
Different Levels

AbstractIn view of the relatively large energy consumption of national building stocks, many cities and municipalities start to prepare energetic building stock models to monitor energy efficiency and plan policies at city or regional scales. In many cases, data on individual buildings is not available. A usual approach to this is the “archetype” approach – classifying the building stock into energetic types (archetypes). This classification is usually based on non-energetic properties available in digital cadastres (construction type, year of construction etc.) and can be a large source of error. We present our research into the difficulties and pitfalls associated with such an approach using the city of Hamburg as an example. In the end, we compare the modelled estimates with consumption data at three different levels to evaluate model performance.

scholarly journals Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 290-302
Author(s):  
Lotta Kannari ◽  
Jussi Kiljander ◽  
Kalevi Piira ◽  
Jouko Piippo ◽  
Pekka Koponen
Keyword(s):  
Machine Learning ◽  
Demand Forecasting ◽  
Office Buildings ◽  
Ann Model ◽  
Short Term ◽  
Equal Distribution ◽  
Advantages And Disadvantages ◽  
Apartment Buildings ◽  
Heat Demand ◽  
Mean Square Errors

Accurate short-term forecasts of building energy consumption are necessary for profitable demand response. Short-term forecasting methods can be roughly classified into physics-based modelling and data-based modelling. Both of these approaches have their advantages and disadvantages and it would be therefore ideal to combine them. This paper proposes a novel approach that allows us to combine the best parts of physics-based modelling and machine learning while avoiding many of their drawbacks. A key idea in the approach is to provide a variety of building parameters as input for an Artificial Neural Network (ANN) and train the model with data from a large group of simulated buildings. The hypothesis is that this forces the ANN model to learn the underlying simulation model-based physics, and thus enables the ANN model to be used in place of the simulator. The advantages of this type of model is the combination of robustness and accuracy from a high-detail physics-based model with the inference speed, ease of deployment, and support for gradient based optimization provided by the ANN model. To evaluate the approach, an ANN model was developed and trained with simulated data from 900–11,700 buildings, including equal distribution of office buildings, apartment buildings, and detached houses. The performance of the ANN model was evaluated with a test set consisting of 60 buildings (20 buildings for each category). The normalized root mean square errors (NRMSE) were on average 0.050, 0.026, 0.052 for apartment buildings, office buildings, and detached houses, respectively. The results show that the model was able to approximate the simulator with good accuracy also outside of the training data distribution and generalize to new buildings in new geographical locations without any building specific heat demand data.

scholarly journals HEAT DEMAND FOR HEATING APARTMENT BUILDINGS/GYVENAMŲJŲ NAMŲ FAKTINIO ŠILUMINĖS ENERGIJOS POREIKIO SULYGINIMAS SU PROJEKTINE ŠILDYMO SISTEMŲ GALIA

2000 ◽  
Vol 6 (5) ◽  
pp. 366-370
Author(s):  
Jūratė Karbauskaitė ◽  
Vytautas Stankevičius
Keyword(s):  
Energy Consumption ◽  
District Heating ◽  
Heating System ◽  
Heat Consumption ◽  
District Heating System ◽  
Apartment Buildings ◽  
Heat Demand ◽  
Heated Area ◽  
Design Values ◽  
Design Value

In this paper the results of statistic analysis of heat consumption in apartment heating systems for Lithuania are discussed. Kaunas district heating system data are used for the analysis. Total sum of buildings involved is about 1900, including 1550 with the average heated area of 4000 m2. It has been established that real heat consumption in apartment buildings is less than the design heat demand (Fig 1), especially in small buildings (Fig 2). The distribution of monthly differences is presented in Fig 3. The difference during months does not depend on average outdoor temperature, but it could be caused by temperature fluctuations and solar radiation. It is quite important to determine the reasons of different heat consumption in buildings. For this purpose 20 dwelling houses of various design and building period, with various energy consumption problems have been selected for more detailed energy audit. Volumes of external building elements, changes in destination of premises, heated area have been estimated as well as the state of heat supply sub-station equipment. According to the data obtained, the energy consumption was determined for standard month at mean indoor and outdoor climate values. The results are compared with real energy consumption in the selected buildings and design values. It has been established that the inadequacies in exceeded energy consumption over design values are mostly caused by incorrect heated area registration and premises destination change, in a less range by absence of maintenance, eg broken outside doors, damaged roofs etc. Energy consumption in dwelling houses with design indoor temperature and normal maintenance level usually is near to the design value or less up to 10%. In dwelling houses, in which energy consumption is defined as being of less design value, some energy saving measures are applied, eg temperature in spaces is lowered up to 16°C, about half of balconies are glassed, electric stoves for cooking are installed as additional heat source. Such apartment buildings, as a rule, do not have premises of other destination. By such means near 40% of heat is saved.

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