Search

Renovation of existing buildings has been highlighted as an essential action in the European Green Deal and in the latest revision of the EPBD to achieve energy efficiency targets. Renovation Wave strategy aims to double the deep energy renovation rate in EU by 2030, while specifically targeting the worst-performing segment of the existing building stock. The objective of this study was to assess the energy performance of deep renovation concepts on single and multi-family houses in three different geographical settings – Germany, Italy and Estonia. Typical German 1970s buildings were used as a baseline reference, and it was shown that very similar renovation concepts can be successfully applied across Europe that provides good bases to develop standardized solutions. Energy performance of two common renovation concepts was assessed both with national calculation methodologies and dynamic simulation with harmonized inputs and detailed heat pump plant models. The renovation concepts included improved envelope insulation and airtightness, switched to heat pump systems and heat recovery ventilation as well as incorporated on-site PV while complying with nearly zero-energy requirements for major renovations in all three countries. National energy calculation methodologies showed good accuracy for before the renovation situation but failed in many cases to calculate adequately NZEB renovation concepts with heat pumps and PV, indicating the development needs towards hourly calculation and more detailed treatment of heat pumps.

Renovation of existing buildings is fundamental to reduce greenhouse gas emissions of the building sector and to ensure the efficient operation of renewable heating systems. In multi-family houses, the suitability of heat pumps is limited by high required temperatures for the hot water preparation, which can be mitigated by hybrid heat pump systems. In this study, the energetic performance of a hybrid heat pump in a multi-family house, built in 1964, is investigated based on field data before and after a renovation. Multiple months are measured and mapped to a full year period. The combination of different renovation measures in the heating system and building envelope is rated w.r.t. their ecological and economical impact by taking into account the actual investment costs. The evaluation shows that the installation of a hybrid heat pump can achieve an accumulated greenhouse gas emissions reduction of 45%, which is similar to a building renovation to a new-build standard, which reduces the space heating demand by up to 62%. Nevertheless, only a combination of both measures can substantially reduce the emissions, which in this case are 81% lower compared to a gas boiler in 1990, which is still below the German climate target for 2040. Due to the low investment costs of a hybrid heat pump system, tenants are more likely to profit from a renting costs reduction, while a building renovation is especially economically beneficial at high energy prices. The results therefore emphasize that the insulation level should be selected carefully, as heat pumps already prepare space heating efficiently and that the heat pump must be able to support the hot water preparation to reach high emission reduction targets. [ABSTRACT FROM AUTHOR]

The District Energy Simulation Test (DESTEST) is a series of common exercises about modelling building stocks and district heating networks aiming at testing, benchmarking and verifying different urban-scale energy system simulation tools. For each common exercise, participants are modelling a case with well-defined characteristics, grid topology and boundary conditions. The DESTEST allows participants to discuss common mistakes and pitfalls and define guidelines from the experience and feedback. These common exercises can also be used for training purposes. This article discusses the development process of these common modelling exercises and presents the main lessons learnt during the creation of the DESTEST.

Revised EPBD directive has set ambitious targets for renovation. It is stated that Member States shall establish a long-term strategy facilitating the cost-effective transformation of existing buildings into nearly-zero energy buildings. The long-term strategy should set out a roadmap with a view to the long-term 2050 goal of reducing greenhouse gas emissions in the European Union. This creates the need for cost-efficient renovation solutions which can be implemented in large scale. The impact assessment shows that roughly a doubled renovation rate of 3 % would be needed to accomplish the energy efficiency ambitions in a cost-effective manner. The objective of this study is to specify renovation concepts with adequate heating and ventilation, based on Estonian and German apartment buildings and corresponding local solutions. Energy performance and sizing analyses were conducted for selected multifamily apartment buildings typical for 1960-70es with three different renovation concepts. Energy calculations were conducted with national energy calculation methods and national energy requirements for major renovation. In the renovation, the building envelope insulation, air tightness, and heating and ventilation systems were improved so that the renovated building complies with national nearly zero-energy requirement for major renovation.

Unrenovated multifamily houses in Germany are mostly heated by fossil heat generators; therefore, measures are required for CO2 emission reduction. The use of air–water heat pumps is restricted by high required flow temperatures and heat output but can be mitigated by hybrid heat pumps. To limit additional operation costs by the heat pump, a new operation strategy is introduced in this study, which allows to maintain a target CO2 emission. The operation strategy is applied in a field trial in a small unrenovated multifamily house built in 1964. A thermohydraulic simulation model is verified and is used in full-year simulations to apply improvement measures and compare the new control strategy with existing optimizing strategies. The results show that the control onto target emissions is possible and limits additional costs but can also result in higher CO2 mitigation costs, making it less favorable. The hybrid heat pump reduces the direct fossil CO2 emissions by 61% (in total by 22%); thus, it is a relevant solution for multifamily houses, especially within a further decarbonized electrical grid. [ABSTRACT FROM AUTHOR]

Using a simulation and optimization environment, this paper presents advances toward near-optimal building thermal mass control derived from full factorial analyses of the important parameters influencing the passive thermal storage process for a range of buildings and climate/utility rate structure combinations. Guidelines for the application of, and expected savings from, building thermal mass control strategies that can be easily implemented and result in a significant reduction in building operating costs and peak electrical demand are sought. In response to the actual utility rates imposed in the investigated cities, fundamental insights and control simplifications are derived from those buildings deemed suitable candidates. The near-optimal strategies are derived from the optimal control trajectory, consisting of four variables, and then tested for effectiveness and validated with respect to uncertainty regarding building parameters and climate variations. Due to the overriding impact of the utility rate structure on both savings and control strategy, combined with the overwhelming diversity of utility rates offered to commercial building customers, this study cannot offer universally valid control guidelines. Nevertheless, a significant number of cases, i.e., combinations of buildings, weather, and utility rate structure, have been investigated, which offer both insights and recommendations for simplified control strategies. These guidelines represent a good starting point for experimentation with building thermal mass control for a substantial range of building types, equipments, climates, and utility rates. [DOI: 10.1115/1.4001466]

In order to avoid high utility demand charges from cooling during the summer and to level a building's electrical demand profile, precooling of the building's massive structure can be applied to shift cooling-related thermal loads in response to utility pricing signals. Several previous simulation and experimental studies have shown that proper precooling can attain considerable reduction of operating cost in buildings. This paper systematically evaluates the merits of the passive building thermal capacitance to minimize energy cost for a design day using optimal control. The evaluation is conducted by means of a sensitivity analysis utilizing a dynamic building energy simulation program coupled to a popular technical computing environment. The optimal controller predicts the required extent of precooling (zone temperature set-point depression), depending on the utility rate structure, occupancy and on-peak period duration and onset, internal gains, building mass, occupancy period temperature set-point range, and weather as characterized by diurnal temperature and relative humidity swings. In addition to quantifying the building response, energy consumption, and utility cost, this paper extracts the dominant features of the optimal precooling strategies for each of the investigated cases so that guidelines for near-optimal building thermal mass savings may be developed in the future. [DOI: 10.1115/1.2770755]

Copyright of Bauphysik is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We present a fully parallelized, high-performance, OpenGL-based approach to compute obstructed area-to-area (A2A) view factors for radiative heat transfer. The A2A view factors are computed from the defining surface integral by Gaussian quadrature. The values of the integrand, i.e. the point-to-area view factors, are computed using an OpenGL-based hemicube (HC) method to efficiently solve the obstruction problem by exploiting the hardware-accelerated visibility testing on modern graphics cards. The final steps to maximize hardware usage are rendering the HC and performing the numerical quadrature in parallel such that data transfer times are completely shadowed by computations. To demonstrate the power of our approach we compute the A2A view factor matrices for a warehouse equipped with ceramic infrared heaters and a test cabin conforming to EN 442 containing a section of a panel radiator. To judge the quality of the results we measure the deviation from unity of the area-weighted column sums of the view factor matrix and the error in radiant flux balance. Compared to a previous, ray-tracing-based implementation, we gain three orders of magnitude in speed in the view factor computation. Conservation of radiant flux is also substantially improved. [ABSTRACT FROM AUTHOR]

The alternative control concept using emission from the machine has the potential to reduce energy consumption in HVAC systems. This paper reports on a study of alternative inputs for a control system of HVAC using machine learning algorithms, based on data that are gathered in a welding area of an automotive factory. A data set of CO 2 , fine dust, temperatures and air velocity was logged using continuous and gravimetric measurements during two typical production weeks. The HVAC system was reduced gradually each day to trigger fluctuations of emission. The data were used to train and test various machine learning models using different statistical indices, consequently to choose a best fit model. Different models were tested and the Long Short-Term Memory model showed the best result, with 0.821 discrepancy on R 2 . The gravimetric samples proved that the reduction of air exchange rate does not correlate to escalation of fine dust linearly, which means one cannot rely on just gravimetric samples for HVAC system optimization. Furthermore, by using machine learning algorithms, this study shows that by using commonly available low cost sensors in a production hall, it is possible to correlate fine dust data cost effectively and reduce electricity consumption of the HVAC. [ABSTRACT FROM AUTHOR]

The cost savings potential of optimal passive thermal storage control were examined for day-ahead real time electricity rate structures. The operational strategies of three office building models were optimized in four US cities (Chicago, New York, Houston and Los Angeles) using price and weather data for the summer of 2008. Optimization of building thermal mass was conducted using a predictive optimal controller to define supervisory strategies in terms of building global cooling temperature setpoints. A global minimization algorithm determined optimal setpoint trajectories for each day divided into four distinct time periods. Cost savings were found to range from 0 to 14% depending on the building, climate, and characteristics of the rate signal. The best cost savings occurred for price spikes or cool nighttime temperatures. Moreover, it was found that low internal gains favoured a more flexible precooling strategy, while high internal gains coupled with low thermal mass resulted in poor precooling performance. [ABSTRACT FROM AUTHOR]

Empirical validation of building energy simulation tools is an important component in assessing the reliability of the simulation software. An experiment performed in conjunction with the International Energy Agency's Task 34/Annex 43 was used to assess the performance of four building energy simulation codes used to model an outdoor test cell with a glazing unit. The experiment was run for a 20-day period during October 2004, and experimental cooling powers were compared with predictions from (1) EnergyPlus, (2) DOE-2.1E, (3) TRNSYS-TUD, and (4) ESP-r. Detailed code inputs for optical and thermophysical properties as well as the impact of thermal bridges were quantified through experiments and simulations; numerous statistical parameters and sensitivity analyses were implemented to facilitate a thorough comparison of predicted and experimental cooling powers. The mean percentage differences for all four codes were: 1.9% for EnergyPlus, -3.6% for DOE-2.1E, -6.2% for TRNSYS-TUD, and 3.1% for ESP-r. The implications of various modeling procedures as well as a detailed discussion of the results are provided, specifically concerning the sensitivity of the code cooling power predictions to the selection of convective heat transfer coefficients and algorithms. [ABSTRACT FROM AUTHOR]

This paper demonstrates model-based predictive optimal control of active and passive building thermal storage inventory in a test facility in real time using time-of-use differentiated electricity prices without demand charges. A novel supervisory controller successfully executed a three-step procedure consisting of (1) short-term weather prediction, (2) optimization of control strategy over the next planning horizon using a calibrated building model, and (3) post-processing of the optimal strategy to yield a control command for the current time step that can be executed in the test facility. All primary and secondary building mechanical systems were effectively orchestrated by the model-based predictive optimal controller in real time while observing comfort and operational constraints. It was determined that even when the optimal controller is given imperfect weather forecasts and when the building model used for planning control strategies does not match the actual building perfectly, measured utility cost savings relative to conventional building operation can be substantial. Central requirements are a facility that lends itself to passive storage utilization and a building model that includes a realistic plant representation. Savings associated with passive building thermal storage inventory proved to be small in this case because the test facility is not an ideal candidate for the investigated control technology. Moreover, the facility's central plant revealed the idiosyncratic behavior that the chiller operation in the ice-making mode was more energy efficient than in the chilled-water mode. Field experimentation is now required in a suitable commercial building with sufficient thermal mass, an active TES system, and a climate conducive to passive storage utilization over a longer testing period to support the laboratory findings presented in this study. [ABSTRACT FROM AUTHOR]

This paper evaluates the benefits of combined optimal control of both passive building thermal capacitance and active thermal energy storage systems to minimize total utility cost in the presence of forecasting uncertainty in the required short-term weather forecasts. Selected short-term weather forecasting models are introduced and investigated with respect to their forecasting accuracy as measured by root mean square error, mean bias error, and the coefficient of variation. The most complex model, a seasonal autoregressive integrated moving average (SARIMA), shows the worst performance, followed by a static predictor model that references standard weather archives. The best prediction accuracy is found for bin models that develop a characteristic daily profile from observations collected over the past 30 or 60 days. The model that projects yesterday's patterns one day into the future proved to be a surprisingly poor predictor. We test the predictor models in the context of a predictive optimal control task that optimizes building global temperature setpoints and active thermal energy storage charge/discharge rates in a closed-loop mode. For the four locations investigated in this article—Chicago, IL, Denver, CO, Omaha, NE, and Phoenix, AZ—it was determined that the 30-day and 60-day bin predictor models lead to utility cost savings that are only marginally inferior compared to a hypothetical perfect predictor that perfectly anticipates the weather during the next planning horizon. In summary, the predictive optimal control of active and passive building thermal storage inventory using time-of-use electrical utility rates with significant on-peak to off-peak rate differentials and demand charges is a highly promising control strategy when perfect weather forecasts are available. The primary finding of this paper is that it takes only very simple short-term prediction models to realize almost all of the theoretical potential of this technology. [ABSTRACT FROM AUTHOR]

Optimisation based economic despatch of real-world complex energy systems demands reduced order and continuously differentiable component models that can represent their part-load behaviour and dynamic responses. A literature study of existing modelling methods and the necessary characteristics the models should meet for their successful application in model predictive control of a polygeneration system are presented. Deriving from that, a rational modelling procedure using engineering principles and assumptions to develop simplified component models is applied. The models are quantitatively and qualitatively evaluated against experimental data and their efficacy for application in a building automation and control architecture is established. [ABSTRACT FROM AUTHOR]