| 1. |
|
|
| 2. |
- Andersson, Staffan, 1952-, et al.
(författare)
-
Building performance based on measured data
- 2011
-
Ingår i: World Renewable Energy Congress – Sweden, 8–13 May, 2011, Linköping, Sweden. - Linköping : Linköping University Electronic Press. - 9789173930703 ; , s. 899-906
-
Konferensbidrag (refereegranskat)abstract
- With increasing liability for builders, the need for evaluation methods that focuses on the building’s performance and thus excludes the impact from residents’ behavior increases. This is not only of interest for new buildings but also when retrofitting existing buildings in order to reduce energy end-use. The investigation in this paper is based on extensive measurements on two fairly representative type of buildings, a single family building in Ekerö, Stockholm built 2000 and two apartment buildings in Umeå (1964) in order to extract key energy performance parameters such as the building’s heat loss coefficient, heat transfer via the ground and heat gained from the sun and used electricity. With access to pre-processed daily data from a 2-month periods, located close to the winter solstice, a robust estimate of the heat loss coefficient was obtained based on a regression analysis. For the single family building the variation was within 1% and for the two heavier apartment buildings an average variation of 2%, with a maximum of 4%, between different analyzed periods close to the winter solstice. The gained heating from the used electricity in terms of a gain factor could not be unambiguously extracted and therefore could only a range for the heat transfer via ground be estimated. The estimated range for the transfer via ground for the two apartment buildings were in very good agreement with those calculated according to EN ISO 13 370 and corresponded to almost 10% of the heating demand at the design temperature. For the single family building with an insulated slab and parts of the walls below ground level, the calculations gave slightly higher transfer than what was obtained from the regression analysis. For the estimated gained solar radiation no comparison has been possible to make, but the estimated gain exhibited an expected correlation with the global solar radiation data that was available for the two apartment buildings.
|
|
| 3. |
|
|
| 4. |
- Olofsson, Thomas, 1968-, et al.
(författare)
-
A method for predicting the annual building heating demand based on limited performance data
- 1998
-
Ingår i: Energy and Buildings. - : Elsevier. - 0378-7788 .- 1872-6178. ; 28:1, s. 101-108
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present an investigation of the possibility to use a neural network combined with a quasi-physical description in order to predict the annual supplied space heating demand (P) for a number of small single family buildings located in the North of Sweden. As a quasi-physical description for P, we used measured diurnal performance data from a similar building or simulated data from a steady state energy simulation software. We show that the required supplied space heating demand may be predicted with an average accuracy of 5%. The predictions were based on access to measured diurnal data of indoor and outdoor temperatures and the supplied heating demand from a limited time period, ranging from 10 to 35 days. The prediction accuracy was found to be almost independent of what time of the year the measurements were obtained from, except for periods when the supplied heating demand was very small. For models based on measurements from May and fo some buildings from April and September, the prediction was less accurate.
|
|
| 5. |
- Olofsson, Thomas, 1968-, et al.
(författare)
-
Energy load predictions for buildings based on a total demand perspective
- 1998
-
Ingår i: Energy and Buildings. - : Elsevier. - 0378-7788 .- 1872-6178. ; 28:1, s. 109-116
-
Tidskriftsartikel (refereegranskat)abstract
- The outline of this work was to develop models for single family buildings, based on a total energy demand perspective, i.e., building-climate-inhabitants. The building-climate part was included by using a commercial dynamic energy simulation software. Whereas the influence from the inhabitants was implemented in terms of a predicted load for domestic equipment and hot water preparation, based on a reference building. The estimations were processed with neural network techniques. All models were based on access to measured diurnal data from a limited time period, ranging from 10 to 35 days. The annual energy predictions were found to be improved, compared to models based on only a building-climate perspective, when the domestic load was included. For periods with a small heating demand, i.e., May-September, the average accuracy was 7% and 4% for the heating and total energy load, respectively, whereas for the rest of the year the accuracy was on average 3% for both heating and total energy load.
|
|
| 6. |
|
|
| 7. |
- Puttige, Anjan Rao, 1990-, et al.
(författare)
-
A Novel Analytical-ANN Hybrid Model for Borehole Heat Exchanger
- 2020
-
Ingår i: Energies. - : MDPI. - 1996-1073. ; 13:23
-
Tidskriftsartikel (refereegranskat)abstract
- Optimizing the operation of ground source heat pumps requires simulation of both short-term and long-term response of the borehole heat exchanger. However, the current physical and neural network based models are not suited to handle the large range of time scales, especially for large borehole fields. In this study, we present a hybrid model for long-term simulation of BHE with high resolution in time. The model uses an analytical model with low time resolution to guide an artificial neural network model with high time resolution. We trained, tuned, and tested the hybrid model using measured data from a ground source heat pump in real operation. The performance of the hybrid model is compared with an analytical model, a calibrated analytical model, and three different types of neural network models. The hybrid model has a relative RMSE of 6% for the testing period compared to 22%, 14%, and 12% respectively for the analytical model, the calibrated analytical model, and the best of the three investigated neural network models. The hybrid model also has a reasonable computational time and was also found to be robust with regard to the model parameters used by the analytical model.
|
|
| 8. |
- Puttige, Anjan Rao, 1990-, et al.
(författare)
-
Application of Regression and ANN Models for Heat Pumps with Field Measurements
- 2021
-
Ingår i: Energies. - : MDPI. - 1996-1073. ; 14:6
-
Tidskriftsartikel (refereegranskat)abstract
- Developing accurate models is necessary to optimize the operation of heating systems. A large number of field measurements from monitored heat pumps have made it possible to evaluate different heat pump models and improve their accuracy. This study used measured data from a heating system consisting of three heat pumps to compare five regression and two artificial neural network (ANN) models. The models’ performance was compared to determine which model was suitable during the design and operation stage by calibrating them using data provided by the manufacturer and the measured data. A method to refine the ANN model was also presented. The results indicate that simple regression models are more suitable when only manufacturers’ data are available, while ANN models are more suited to utilize a large amount of measured data. The method to refine the ANN model is effective at increasing the accuracy of the model. The refined models have a relative root mean square error (RMSE) of less than 5%
|
|
| 9. |
- Puttige, Anjan Rao, 1990-, et al.
(författare)
-
Improvement of borehole heat exchanger model performance by calibration using measured data
- 2020
-
Ingår i: Journal of Building Performance Simulation, Taylor & Francis. - : Taylor & Francis. - 1940-1493 .- 1940-1507. ; 13:4, s. 430-442
-
Tidskriftsartikel (refereegranskat)abstract
- Planning the operation of large ground source heat pump (GSHP) systems requires accurate models of borehole heat exchangers (BHEs) that are not computationally intensive. In this paper, we propose parameter estimation using measured data as a method to improve the analytical models of BHE. The method was applied to a GSHP system operating for over 3 years. The deviation between modelled and measured load of the BHE reduced from 22% to 14%. Influence of the calibration data set was tested by changing time resolution and season of the calibration data. We concluded that the time resolution must be high enough to differentiate among the effects of different parameters and that different model parameters must be used for injection and extraction (seasons). The method was also applied to a GSHP that has been monitored for 10 years, which showed that accuracy of the model can be improved by annual updates of parameters.
|
|
| 10. |
- Puttige, Anjan Rao, 1990-, et al.
(författare)
-
Method to estimate the ground loads for missing periods in a monitored GSHP
- 2019
-
Ingår i: EUROPEAN GEOTHERMAL CONGRESS 2019.
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Monitoring a ground source heat pump can provide important insights into its working, but to study the behaviour of the borehole heat exchanger (BHE) we require monitored data for the whole period of operation. In practice, the monitored data often has periods of missing data. We propose a method to estimate the load during the periods of missing data based on the fluid temperature after that period. The method determined the missing load with negligible error, for the case of a BHE that behaves exactly like the model describing it. A sensitivity analysis showed that the estimated load is highly sensitive to errors in measured load and fluid temperature. The method was applied to a real monitored BHE, the magnitude of estimated loads were unreasonably high, but the overall deviation between the measured and simulated values of fluid temperature decreased. Therefore, the high magnitude of missing load compensates for the lack of agreement between the model and the measured data.
|
|
| 11. |
- Puttige, Anjan Rao, 1990-, et al.
(författare)
-
Modeling and optimization of hybrid ground source heat pump with district heating and cooling
- 2022
-
Ingår i: Energy and Buildings. - : Elsevier. - 0378-7788 .- 1872-6178. ; 264
-
Tidskriftsartikel (refereegranskat)abstract
- Hybrid heating systems with ground source heat pumps (GSHP) and district heating and cooling offer flexibility in operation to both building owners and energy providers. The flexibility can be used to make the heating system more economical and environmentally friendly. However, due to the lack of suitable models that can accurately predict the long-term performance of the GSHP, there is uncertainty in their performance and concerns about the long-term stability of the ground temperature, which has limited the utilization of such hybrid heating systems. This work presents a hybrid model of a GSHP system that uses analytical and artificial neural network models to accurately represent a GSHP system's long-term behavior. A method to improve the operation of a hybrid GSHP is also presented. The method was applied to hospital buildings in northern Sweden. It was shown that in the improved case, the cost of providing heating to the building can be reduced by 64 t€, and the CO2 emissions can be reduced by 92 tons while maintaining a stable ground temperature.
|
|
| 12. |
- Puttige, Anjan Rao, 1990-
(författare)
-
Utilization of a GSHP System in a DHC Network : modeling and optimization
- 2021
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The ground source heat pumps (GSHPs) of customers connected to the district heating and cooling (DHC) network can benefit both the customer and the energy company. However, operating the GSHP to minimize the cost of providing heating and cooling to the customer while ensuring the long-term stability of the ground temperature is a challenge. This thesis addresses the challenge by developing accurate models of GSHP and optimizing the operation of the GSHP system using these models.The models presented in this thesis use field measurements to develop accurate models with low computational time. The main components of a GSHP system are the heat pump and the borehole heat exchanger (BHE). This thesis presents two approaches to use measured data to improve the accuracy of analytical models for BHE. The first approach is the calibration of the model parameters using this measured data. The second approach combines the analytical model with an artificial neural network model resulting in a hybrid model. The calibration approach reduced the relative RMSE of the analytical model from 21.9% to 13.9% in the testing period. The relative RMSE of the hybrid model for the testing period was 6.3%.We compared different data-driven models for heat pumps and determined that artificial neural network models have an advantage over traditional regression models when field measurements are available. The artificial neural network model was refined to better utilize the measured data. The refined models of heat pumps had a relative RMSE of less than 5%.The hybrid BHE model and an artificial neural network model for the heat pumps were used to model the GSHP system. The model was validated using four years of field measurements. The relative MAE for the compressor power and BHE power were 7.3% and 19.1% respectively.The validated model was used to optimize the operation of the GSHP system. In optimal operation, the cost of providing heating and cooling to the area was minimized from the perspective of the energy company while maintaining a stable temperature in the ground. In optimal operation, the annual cost of operation was shown to reduce by 64 t€ and the annual CO2 emission was shown to reduce by 92 tons.
|
|
