• Energy Research
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Passive buildings compared to the standard ones require significantly less energy for heating, so the correct models of every “energy using” building's components are very important. This paper analyzes how various models of the internal heat and moisture gains, as well as natural airflows between building zones, influence the accuracy of the calculation of the energy performance, indoor temperatures and absolute humidity in a single-family passive building. A simulation environment used a detailed twelve-zone TRNSYS model of a house with HVAC system. The model included natural airflows between zones, and internal heat and moisture gains, defined as precisely as possible. The gains were allocated on the basis of special protocols of use filled by the occupants during the two-week measurement. The measurement data were also used for validation of the model. The verified model constituted a basis for calculation of energy performance and simulation of air temperature and absolute humidity change in a building with significantly limited airflow between zones, and heat and moisture gains defined according to standards. The standardized values of heat and moisture gains were defined on the basis of the standard ISO 13790 and national regulations in Poland. The simulations have shown that precise methodology of calculation of heat gains and airflows between building zones is very important for proper computation of energy performance and simulation of indoor temperatures and absolute humidity in passive buildings. Results of carried out analysis have shown that the difference in energy need for heating calculated using precise and simplified methods of internal heat gains determination was 30.1%.

This paper elaborates different hybrid energy scenarios for applications in small or medium residential building facilities in typical Mediterranean climate conditions. Proposed hybrid energy solutions are based on the split air conditioning units as they are the most used individually energy sources able to cover heating and cooling demands in residential building facilities in considered climate conditions. Four different hybrid energy options have been analysed as they represent highly efficient energy solutions that could reduce overall energy consumption as well as also carbon dioxide emissions from residential sector. For each proposed energy solution a technical aspect was addressed, as well as also an economic aspect through obtained LCOE analysis. Therefore, the proposed and analysed hybrid energy concepts turned out to be a viable energy solution for residential applications as calculated LCOE was competitive with current retail prices of electricity on the EU market. The calculated LCOE ranged from 0.036 €/kWh up to 0.159 €/kWh, depending from the considered hybrid energy solution. The solution with the modified split heat pump system turned out to be most favourable. However, as each solution is characteristic one an additional evaluation framework was developed. Finally, the results of this study could be useful as examples of good practice as well as also a useful guidance for policy makers.

Abstract Traditional whole building energy modeling suffers from several factors, including the large number of inputs required for building characterization, simplifying assumptions, and the gap between the as-designed and as-built building. Prior work has attempted to mitigate these problems by using sensor-based machine learning approaches to statistically model energy consumption, applying the techniques primarily to commercial building data, which makes use of hourly consumption data. It is unclear, however, whether these techniques can translate to residential buildings, since the energy usage patterns may vary significantly. Until now, most residential modeling research only had access to monthly electrical consumption data. In this article, we report on the evaluation of seven different machine learning algorithms applied to a new residential data set that contains sensor measurements collected every 15 min, with the objective of determining which techniques are most successful for predicting next hour residential building consumption. We first validate each learner's correctness on the ASHRAE Great Energy Prediction Shootout, confirming existing conclusions that Neural Network-based methods perform best on commercial buildings. However, our additional results show that these methods perform poorly on residential data, and that Least Squares Support Vector Machines perform best – a technique not previously applied to this domain.

Abstract The primary goal of the project is to design a lighting system that provides the highest visual comfort and conserves energy for different uses and furniture layouts, which considers that the design of the house will be used by many different users. Indoor system designs vary in layout, goals and optics with two levels of intervention: when initially furnished and during daily use. Lighting is managed by a building automation system, which is equipped with motion and daylight sensors and human interfaces with user scenarios. An outdoor design that requires only a standalone system and is powered by photovoltaic and eolic devices has been developed. All systems incorporate LED technology and have been custom-developed for Med in Italy, which resulted in new patents. Lighting system projects have been involved in several competitions, e.g., Med in Italy placed in the Solar Decathlon in Architecture, Electrical Energy Balance, House Functioning, and Sustainability and took third place in the final ranking. Further research developments are in progress and will be seen in next Solar Decathlon Europe in Paris in 2014. This paper describes the project, installation and analyses of the energy savings that the Medinitaly system can achieve compared with traditional solutions. Simulation data are compared with measured data from the period of the Solar Decathlon competition.

This paper describes the methodology developed and the calculation steps used to evaluate the energy efficiency potential of office buildings. The methodology enables a detailed analysis of retrofit options for the building envelope and its energy supply system. Different simplification measures accelerate the data acquisition process for office building stock owners and allow a data handling according to the existing building information, thus enabling office building structures to be prompted to design typical building constructions. We implement solutions enabling both a time-saving accelerated data input for office buildings and the handling of incomplete data. An automated calculation of the most common refurbishment measures allows a comparison of up to 64 combinations of measures, the illustration of energy and CO2 savings, and an economic evaluation. The latter takes into account the time value of money, the uncertainty of future energy prices, and the possibility of delaying an investment. To this end, a net present value analysis and a real options analysis are implemented, enabling a comparison of retrofit alternatives with different initial and future cash flows both for buildings occupied by the investor (owner-occupier perspective) and for rented buildings (tenant perspective). Energy price scenarios as well as a Monte Carlo simulation account for the uncertainty in energy price trends. For a university building used as a test case, the simplified and time-saving data input methods were successfully tested and an automated evaluation of 64 typical retrofit combinations carried out. The results of the energy, ecological and economic efficiency evaluation shows that a generally preferred retrofit option cannot always be identified. Specifically, for the test case, the best-rated economic refurbishment possibility leads to the largest increase in final energy demand amongst all options considered, which points out the necessity of a multi-criteria evaluation.

Abstract Residential air conditioning equipment comprises a significant portion of the total energy consumption of a home. Unfortunately, air-conditioning systems can be susceptible to faulty operation either from installation errors or faults that accrue over the equipment’s lifetime. This paper presents a novel automated fault detection algorithm for residential air-conditioning systems that can alert the homeowner of the presence of these faults. The proposed algorithm utilizes only the home’s thermostat and outside air temperature to perform automated fault detection over the course of the equipment’s lifetime, including immediately after installation. The algorithm uses an extended Kalman filter approach to identify a three-resistor, two-capacitor (3R2C) electrical equivalent thermodynamic model. The identified 3R2C model is used to predict cooling times during a testing period comprising of a series of thermostat drive-cycle experiments. We tested the algorithm on an EnergyPlus™ model of a typical residential building in Orlando, Florida. Duct faults, indoor airflow faults, and refrigerant undercharge faults were introduced into the building model one at a time. The algorithm was able to accurately determine duct-leak faults, 40% airflow faults, 40% undercharge faults, and no-fault cases with an accuracy of 70%, 77%, 82%, and 87%, respectively.

In numerous fields, especially that of geothermal energy, we need to know about the thermal behaviour of the soil now that the monitoring of renewable forms of energy is an ecological, economic and scientific issue. Thus heat from the soil is widely used for air-conditioning systems in buildings both in Canada and in the Scandinavian countries, and it is spreading. The effectiveness of this technique is based on the soils calorific potential and its thermophysical properties which will define the quality of the exchanges between the soil and a heat transfer fluid. This article puts forward a method to be used for the in situ thermophysical characterisation of a soil. It is based upon measuring the heat exchanges on the surface of the soil and on measuring a temperature a few centimetres below the surface. The system is light, inexpensive, well-suited to the taking of measurements in situ without the sensors used introducing any disturbance into the heat exchanges. Whereas the majority of methods require excitation, the one presented here is passive and exploits natural signals. Based upon a few hours of recording, the natural signals allow us to identify the soils thermophysical properties continuously. The identification is based upon frequency methods the quality of which can be seen when the thermophysical properties are injected into a model with finite elements by means of a comparison of the temperatures modelled and those actually measured on site.

Abstract Ice on wind turbine blades, high-voltage transmission line and aircraft wing is a serous problem in winter, which causes much losses of energy. There is always a strong demand for de-icing techniques that are effective, low in energy consumption, lower cost and reliable operation. Ultrasonic de-icing, as an new de-icing technique, attracts more attention due to its energy-saving, simplicity, lower expenses, good applicability and no pollution produced to environment. In this paper, recent progress on ultrasonic de-icing technique is summarized in view of wind turbine blade de-icing, high-voltage transmission line de-icing and aircraft wing de-icing. In particular, a plier-shaped device invented by a college student in China for high voltage transmission line ultrasonic de-icing is introduced. This device has won China national patent and been effectively put into practical use. Finally, the miniaturization of ultrasonic generator is proposed. The purpose of this paper is to provide theoretic and experimental support for the widespread application of ultrasonic de-icing technique.