•A detailed measurement setup was implemented to test current thermal characteristics of a naturally ventilated double skin facade.•A new methodology for the estimation of facade energy effectiveness ...based on air cavity enthalpy flow is shown.•For each regime (winter, transitional and summer) selected results are discussed and highlighted.•In order to quantify current facade effectiveness, the behaviour of the performance indicator is described.
This study presents the results of the actual thermal behaviour of a multi-storey naturally ventilated double skin facade. Governed by lack of experimentally measured data, a field of detailed measurements were performed during the 2013/2014 season in the office building located in Belgrade, Serbia. The uniqueness of this building is that the first facade layer is made in the tradition manner. Apart from studying classical environmentally influenced air cavity behaviour, the methodology incorporates a detailed analysis of the air cavity enthalpy flow. The main purpose of this research is to examine the current state of the building facade and how it affects energy performance. To this effect, measurement data was used in order to analyse transmission losses and gains and to quantify enthalpy change of the cavity air linked to the airflow established within the facade. Analyse of the transmission losses and gains were described by a series of diagrams of vertical and horizontal temperatures trends for selected typical days in each regime. For investigation of the natural ventilation potential, an adequate indicator based on the enthalpy change was established. Overall, the experiment results highlight that the use of a double skin facade does not necessarily reduce energy consumption.
Buildings use a significant amount of primary energy and largely contribute to greenhouse gases emission. Cost optimality and cost effectiveness, including cost-optimal operation, are important for ...the adoption of energy efficient and environmentally friendly technologies. The long-term assessment of buildings-related greenhouse gases emission might take into account cost-optimal operation of their energy systems. This is often not the case in the literature. Long-term operation optimization problems are often of large scale and computationally intensive and time consuming.
This paper formulates a bottom-up methodology relying on an efficient, but precise operation optimization approach, applicable to long-term problems and use with buildings simulations. We suggest moving-horizon short-term optimization to determine near-optimal operation modes and show that this approach, applied to flexible energy systems without seasonal storage, have satisfactory efficiency and accuracy compared with solving problem for an entire year. We also confirm it as a valuable pre-solve technique.
Approach applicability and the importance of energy systems optimization are illustrated with a case study considering buildings envelope improvements and cogeneration and heat storage implementation in an urban residential settlement. EnergyPlus is used for buildings simulations while mixed integer linear programming optimization problems are constructed and solved using the custom-built software and the branch-and-cut solver Gurobi Optimizer.
•Bottom-up approach for greenhouse gases emission assessment is presented.•Short-term moving-horizon optimization is used to define operation regimes.•Operation optimization and buildings simulations are connected with modeling tool.•Illustrated optimization method performed efficiently and gave accurate results.
Simulation-assisted operation can offer flexibility in terms of prioritizing either building energy consumption or occupant thermal comfort, and in most cases, it can address these issues ...simultaneously. Simulation-assisted operation is the type of operation that uses the knowledge of future disturbances acting on the building, thus allowing the HVAC system operation in such a way so as to meet set goals in terms of building energy consumption and occupant thermal comfort. The most important future disturbances acting on the building are weather and occupant behavior (expectations of thermal environment). To achieve simulation-assisted operation, optimization methods are necessary. This paper presents the methodology to create daily operation strategies for existing HVAC systems serving a real building. The methodology relies on the extensive use of the building energy performance simulation software EnergyPlus, available weather data from the building site or from official weather databases including short-term forecasts, global sensitivity analysis and a custom-built optimization environment based on the particle swarm optimization method, all applied over a shifting (moving) horizon. Global sensitivity analysis with Latin hyper-cube sampling is used to reduce the number of independent variables for the optimization process. The methodology was demonstrated for the office part of an existing real building located on the outskirts of the City of Niš, Serbia. The obtained results show that the application of sensitivity analysis as a pre-step to optimization leads to similar data related to energy consumption and occupant thermal comfort, but with a significant decrease in the use of computational resources.
One of the possible ways to improve balance between building energy consumption and occupant thermal comfort in existing buildings is to use simulation-assisted operation of HVAC systems. ...Simulation-assisted operation can be formulated as a type of operation that implements knowledge of future disturbance acting on the building and that enables operating the systems in such a way to fulfill given goals, which in nature can often be contradictory. The most important future conditions on building energy consumption are weather parameters and occupant behavior and expectations of thermal environment. In order to achieve this type of operation, optimization methods must be applied. Methodology to create HVAC system operation strategies on a daily basis is presented. Methodology is based on using building energy performance simulation software EnergyPlus, available weather data, global sensitivity analysis, and custom developed software with particle swarm optimization method applied over the moving horizon. Global sensitivity analysis is used in order to reduce number of independent variables for the optimization process. The methodology is applied to office part of real combined-type building located in Niš, Serbia. Use of sensitivity analysis shows that the reduced number of independent variables for the optimization would lead to similar thermal comfort and energy consumption, with significant computer runtime reduction.
Procesom decentralizacije gradovi su postali vlasnici velikog broja nekretnina. Međutim, praksa upravljanja tim nekretninama nije ujednačena, pa je otvoreno pitanje koliko učinkovito gradovi ...upravljaju svojim nekretninskim portfeljem. Znanstveni problem rada proizlazi iz nepostojanja sustavne brige o nekretninama u vlasništvu gradova, a posebice nepostojanja primjerenog mjerila (benchmarka) za mjerenje uspješnosti upravljanja nekretninama. Cilj rada je utvrditi obilježja gradova koja utječu na učinkovitost upravljanja nekretninama u vlasništvu gradova. U radu se korištenjem sekundarnih podataka, analizom utemeljenom na neparametarskoj metodi omeđivanja podataka (eng. data envelopment analysis, DEA) i Tobit panel regresijama analizira učinkovitost upravljanja nekretninama u vlasništvu gradova i utvrđuju se čimbenici koji utječu na učinkovitost upravljanja nekretninama. Konkretno, istražuje se upravljaju li gradovi u Hrvatskoj učinkovito nekretninama u svojem vlasništvu, upravljaju li veliki gradovi nekretninama učinkovitije od malih, upravljaju li gradovi smješteni uz more nekretninama učinkovitije od gradova u unutrašnjosti te upravljaju li gradovi koji su sjedišta županija učinkovitije nekretninama od ostalih gradova. Empirijski rezultati u radu upućuju kako gradovi u Hrvatskoj ne upravljaju učinkovito nekretninama u svojem vlasništvu, da veliki gradovi nekretninama u svojem vlasništvu upravljaju učinkovitije od malih gradova, da gradovi uz more učinkovitije upravljaju nekretninama od gradova u unutrašnjosti te da gradovi koji su sjedišta županija učinkovitije upravljaju nekretninama od ostalih gradova.
Through the process of decentralization, cities became the owners of a large number of real estates. However, the practice of managing these properties is not uniform, so the question is how efficiently cities manage their real estate portfolios. The scientific problem of this paper arises from the lack of systematic care for real estate owned by cities, and in particular the lack of an appropriate benchmark for measuring the success of real estate management. The aim of this paper is to determine the characteristics of cities that affect the efficiency of real estate management owned by cities. Using secondary data, based on the non-parametric method of data envelopment analysis (DEA) and Tobit panel regressions, the paper analyzes the efficiency of real estate management owned by cities and identifies the factors that affect the efficiency of real estate management. In particular, the paper investigates whether cities in Croatia efficiently manage their own real estate, whether large cities manage real estate more efficiently than small ones, whether cities located by the sea manage real estate more efficiently than inland cities, and whether cities that are county-centers manage real estate more efficiently than other cities. Empirical results show that cities in Croatia do not manage their real estate efficiently, that large cities manage their real estate more efficiently than small cities, that seaside cities manage real estate more efficiently than inland cities, and that county-based cities manage real estate more efficiently than other cities.
Although fully automated, operation of the District Heating Systems (DHS) is considered reactive and simplistic since control decisions are most often made based on the real-time ambient temperature. ...The research presented in this paper is aiming at facilitating proactive DHS control, by using predictive modeling techniques based on modern Artificial Intelligence (AI) architectures, namely the Deep Learning (DL)-based multivariate time-series forecasting. Proactive approach involves controlling fluid temperature in the DHS supply line, based on relevant meteorological features, heat demand in the past and weather forecast. In this paper, we evaluate state-of-the-art DL architectures for multivariate one-step and multi-step prediction based on Long-Short Term Memory (LSTM) approach, namely, stacked LSTM, bi-directional LSTM, encoder-decoder and attention models. The initial results of forecasting heat demand and local explanations are provided for the case of small 8MW DHS. For local explanations, Local Interpretable Model-agnostic Explanations (LIME) was successfully validated.
The use of air-source heat pumps (ASHP) is increasing to meet the energy needs of residential buildings, and manufacturers of equipment have permanently expanded the range of work and improved the ...efficiency in very adverse outdoor air conditions. However, in the time of a wide range of different technologies, the problem of using ASHP, from a techno-economic point of view, is constantly present. Exergetic efficiency and exergoeconomic cost no longer provide sufficiently reliable information when it is necessary to reduce the investment costs or in-crease the energy/exergetic efficiency of the component/system. This paper presents comparison of ASHP in different operational conditions based on an advanced exergy and exergoeconomic approach. The advanced exergy analysis splits the destruction of exergy for each individual component into avoidable and unavoidable part in order to fully understand the processes. The information of stream costs is used to calculate exergoeconomic variables associated with each system component. Irreversibility in the compressor have the greatest impact on reducing the overall system exergetic efficiency by 46.7% during underfloor heating (UFH) operation and 24.53% during domestic hot water (DHW) operation. Exergy loss reduces exergetic efficiency by 5.72% UFH and 39.74% DHW. High values of exergoeconomic cost for both operating regimes are present in flows 1, 2, 3 and 4 due to high costs of production and relatively small exergy levels. The general recommendation is to set the ASHP to operate with near-optimal capacities in both regimes and then reduce exergy of flows 1, 2, 5, 11, and 13.
Data-driven black-box surrogate models are widely used in research related to buildings energy efficiency. They are based on machine learning techniques, learn from available data, and act as a ...replacement for or an addition to complex and computationally intensive knowledge-based models. Surrogate models can predict energy demand, indoor air temperature, or occupants behavior, explore search space in optimization problems, learn control rules, etc. This paper analyzes surrogate models that classify building retrofit measures directly according to the global cost. In addition, they quantify the importance of each variable for the classification process. The models are based on random forest classifiers, which are fast and powerful ensemble learners. They can be applied to effectively reduce search spaces when optimizing energy renovation measures or to rapidly identify projects that deserve financial support. This approach is applied to two residential buildings and three scenarios of price development. The training process uses a small share of retrofit options assessed with standard calculations of the heating and cooling demands, as well as the global cost. The results show very high classification performance, even when the models are trained with small and imbalanced training sets. The obtained recall, precision, and F-score values are mostly above 95%, except for extremely small training sets.
Relying on coal as primary fuel in thermal power plants represents an
unsustainable concept due to limited coal reserves and a negative
environmental impact. Efficient utilization of coal reserves ...and a request
for minimization of irreversibilities are imperative for thermal power
plants operation. Numerous studies have shown that a steam boiler is a
thermal power plant component with the highest irreversibility. The idea of
this paper is to quantify the amounts and sources of irreversibilities
within a steam boiler and its components, serving a 348.5MWe thermal power
plant. Having this in mind, exergy and exergoeconomic analysis of a steam
boiler is presented in this paper. Exergy destruction and exergy efficiency
of all boiler components and of the boiler as a whole were calculated.
Based on exergy flows and economic parameters (cost of the boiler, annual
operation hours of the unit, maintenance factor, interest rate, operating
period of the boiler), exergy analysis resulted in the cost of produced
steam. The obtained results show that the boiler exergy efficiency is at
47.4%, with the largest exergy destruction occurring in the combustion
chamber with a value of 288.07 MW (60.04%), and the smallest in the air
heater with a value of 4.57 MW (0.95%). The cost of produced steam is
calculated at 49,356.7 $/h by applying exergoeconomic analysis.
nema
Buildings are significant energy consumers and provide a notable potential to reduce primary energy consumption and increase energy efficiency. Cost-effectiveness of energy efficiency projects is of ...crucial importance for their implementation. Cost-optimality of different packages of energy retrofit measures is studied across the EU, but Serbia mostly lacks such information. This paper analyzes cost-optimal solutions for Serbian residential buildings connected to district heating systems, considering three different scenarios related to the economic input parameters. Additionally, it considers the potential for primary energy savings beyond cost-optimality and associated costs. The optimal solutions, that correspond to minimal global cost or minimal primary energy consumption, are determined as the results of the combinatorial optimization problems. These problems are solved using the genetic algorithm and local search. The results are compared against the ones obtained with the sensitivity analysis. The global cost can be reduced by 8-43% in the cases of cost-optimal solutions, simultaneously saving 30-76% of primary energy. The potential to save primary energy is higher - it exceeds 70% in all the analyzed cases, but also requires higher global cost, sometimes larger than in the absence of the retrofit. The paper also emphasizes high dependencies of the results on very uncertain economic inputs.
nema
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