High resolution performance analysis of micro-trigeneration in an energy-efficient residential building

Trigeneration has long been proposed as a means to improve energy-efficiency for large and medium sized buildings. To curb increasing energy demand in the residential sector, researchers are now focusing their attention on adapting trigeneration to residential buildings. Literature is full of examples pertaining to the performance of trigeneration in large and medium sized commercial buildings, however little is known on the performance of micro-trigeneration inside residential buildings, particularly under a range of operating conditions. To understand the influence that parameters such as changes in thermal and electrical loading or different plant configurations have on the performance of micro-trigeneration, this research makes use of a detailed model of a Maltese apartment building, and associated micro-trigeneration system. The performance of the model is simulated using a whole building simulation tool run at high-resolution minute time frequency over a number of different operating conditions and scenarios. Each scenario was then assessed on the basis of the system's energetic, environmental and economic performance. The results show that, compared to separate generation the use of a residential micro-trigeneration system reduces primary energy consumption by about 40%, but also that the system's financial performance is highly susceptible to the operating conditions

Cool Roof Impact on Building Energy Need: The Role of Thermal Insulation with Varying Climate Conditions

Cool roof effectiveness in improving building thermal-energy performance is affected by different variables. In particular, roof insulation level and climate conditions are key parameters influencing cool roofs benefits and whole building energy performance. This work aims at assessing the role of cool roof in the optimum roof configuration, i.e., combination of solar reflectance capability and thermal insulation level, in terms of building energy performance in different climate conditions worldwide. To this aim, coupled dynamic thermal-energy simulation and optimization analysis is carried out. In detail, multi-dimensional optimization of combined building roof thermal insulation and solar reflectance is developed to minimize building annual energy consumption for heating-cooling. Results highlight how a high reflectance roof minimizes annual energy need for a small standard office building in the majority of considered climates. Moreover, building energy performance is more sensitive to roof solar reflectance than thermal insulation level, except for the coldest conditions. Therefore, for the selected building, the optimum roof typology presents high solar reflectance capability (0.8) and no/low insulation level (0.00-0.03 m), except for extremely hot or cold climate zones. Accordingly, this research shows how the classic approach of super-insulated buildings should be reframed for the office case toward truly environmentally friendly buildings.The work was partially funded by the Spanish government (RTI2018-093849-B-C31). This work was partially supported by ICREA under the ICREA Academia programme. Dr. Alvaro de Gracia has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 712949 (TECNIOspring PLUS) and from the Agency for Business Competitiveness of the Government of Catalonia. This publication has emanated from research supported (in part) by Science Foundation Ireland (SFI) under the SFI Strategic Partnership Programme Grant Number SFI/15/SPP/E3125

Waste Not, Want Not: The Potential for Urban Water Conservation in California

The largest, least expensive, and most environmentally sound source of water to meet California's future needs is the water currently being wasted in every sector of our economy. This report, "Waste Not, Want Not," strongly indicates that California's urban water needs can be met into the foreseeable future by reducing water waste through cost-effective water-saving technologies, revised economic policies, appropriate state and local regulations, and public education

Analysis of a residential building energy consumption as “base model” in Tripoli, Lebanon

The interest in energy performance of buildings in Lebanon has increased in the last few years. Indeed, many organizations are evaluating the commercial buildings’ energy performance in order to increase the commercial sector energy efficiency. Since residential buildings occupy 47% of the overall end-use energy consumption in Lebanon, therefore; the development of a rating methodology for residential energy performance should also be significant. This study reveals the results of a field survey of residential apartment buildings in the city of Tripoli. The survey focuses on the newly built-up extended zones of the city (Basateen El-Mina and Basateen Trablous), that are subject to the current building code. Based on a questionnaire and a monitoring survey, a building performance simulation model was created to reflect the average energy consumption characteristics for the most residential building accumulation. This benchmark model describes the energy use report for heating, cooling, lighting, domestic hot water systems and appliances with respect to the building’s layout, orientation and construction. The output data of the simulation will be compared with collected EDL (Electicite du Liban) bill. The aim of this study is to develop representative building energy data sets and benchmark models for the Lebanese residential sector specifically in the coastal zone area. Having a “base model “as a benchmark for existing residential buildings will form the basis of a research on specific building technologies and measurements of progress towards the Zero Energy Building goal

Comparison of software models for energy savings from cool roofs

A web-based Roof Savings Calculator (RSC) has been deployed for the United States Department of Energy as an industry-consensus tool to help building owners, manufacturers, distributors, contractors and researchers easily run complex roof and attic simulations. RSC simulates multiple roof and attic technologies for side-by-side comparison including reflective roofs, different roof slopes, above sheathing ventilation, radiant barriers, low-emittance roof surfaces, duct location, duct leakage rates, multiple substrate types, and insulation levels. Annual simulations of hour-by-hour, whole-building performance are used to provide estimated annual energy and cost savings from reduced HVAC use. While RSC reported similar cooling savings to other simulation engines, heating penalty varied significantly. RSC results show reduced cool roofing cost-effectiveness, thus mitigating expected economic incentives for this countermeasure to the urban heat island effect. This paper consolidates comparison of RSC's projected energy savings to other simulation engines including DOE-2.1E, AtticSim, Micropas, and EnergyPlus. Also included are comparisons to previous simulation-based studies, analysis of RSC cooling savings and heating penalties, the role of radiative heat exchange in an attic assembly, and changes made for increased accuracy of the duct model. Radiant heat transfer and duct interaction not previously modeled is considered a major contributor to heating penalties

Energy use in residential buildings: Impact of building automation control systems on energy performance and flexibility

This work shows the results of a research activity aimed at characterizing the energy habits of Italian residential users. In detail, by the energy simulation of a buildings sample, the opportunity to implement a demand/response program (DR) has been investigated. Italian residential utilities are poorly electrified and flexible loads are low. The presence of an automation system is an essential requirement for participating in a DR program and, in addition, it can allow important reductions in energy consumption. In this work the characteristics of three control systems have been defined, based on the services incidence on energy consumptions along with a sensitivity analysis on some energy drivers. Using the procedure established by the European Standard EN 15232, the achievable energy and economic savings have been evaluated. Finally, a financial analysis of the investments has been carried out, considering also the incentives provided by the Italian regulations. The payback time is generally not very long: depending on the control system features it varies from 7 to 10 years; moreover, the automation system installation within dwellings is a relatively simple activity, which is characterized by a limited execution times and by an initial expenditure ranging in 1000 € to 4000 €, related to the three sample systems

Machine Learning Applications in Estimating Transformer Loss of Life

Transformer life assessment and failure diagnostics have always been important problems for electric utility companies. Ambient temperature and load profile are the main factors which affect aging of the transformer insulation, and consequently, the transformer lifetime. The IEEE Std. C57.911995 provides a model for calculating the transformer loss of life based on ambient temperature and transformer's loading. In this paper, this standard is used to develop a data-driven static model for hourly estimation of the transformer loss of life. Among various machine learning methods for developing this static model, the Adaptive Network-Based Fuzzy Inference System (ANFIS) is selected. Numerical simulations demonstrate the effectiveness and the accuracy of the proposed ANFIS method compared with other relevant machine learning based methods to solve this problem.Comment: IEEE Power and Energy Society General Meeting, 201

Screening of energy efficient technologies for industrial buildings' retrofit

This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit