| 1. |
- Aspetakis, Giorgos, et al.
(author)
-
Critical review of Air-Based PVT technology and its integration to building energy systems
- 2023
-
In: Energy and Built Environment. - : Elsevier BV. - 2666-1233.
-
Research review (peer-reviewed)abstract
- Climate crisis mitigation roadmaps, policies and directives have increasingly declared that a key element for the facilitation of sustainable urban development is on-site decentralized renewable energy generation. A technology with enhanced capabilities, able of promoting the integration of renewable energy into buildings, for energy independent and resilient communities, is Photovoltaic Thermal (PVT) systems. Ongoing research has potential yet displays a lack in unified methodology. This limits its influence on future decision-making in building and city planning levels. In this investigation, the often overlooked air-based PVT technology is put on the spotlight and their suitability for integration with energy systems of buildings is assessed. The aim of this study is to highlight vital performance and integration roadblocks in PVT research and offer suggestions for overcoming them. The methodology of reviewed literature is examined in detail with the goal of contributing to a unified approach for more impactful research.
|
|
| 2. |
- Camarasa, Clara, 1986, et al.
(author)
-
Drivers and barriers to energy-efficient technologies (EETs) in EU residential buildings
- 2021
-
In: Energy and Built Environment. - : Elsevier BV. - 2666-1233. ; 2:3, s. 290-301
-
Journal article (peer-reviewed)abstract
- To achieve carbon targets, the European Union (EU) aims to promote nearly zero-energy buildings (nZEB). To enable the necessary transition, technical solutions need to converge with socio-economic factors, such values and awareness of stakeholders involved in the decision-making process. In this light, the aim of this paper is to characterise perceived drivers and barriers to nine energy-efficient technologies (EET), according to key decision-makers' and persuaders of the technology selection in the EU residential building context. Results are collected across eight EU countries, i.e. Belgium (BE), Germany (DE), Spain (ES), France (FR), Italy (IT), Netherlands (NL), Poland (PL), and United Kingdom (UK). The stakeholders’ selected are architects, construction companies, engineers, installers and demand-side actors. Data from a multi-country survey is analysed to calculate the share of 15 drivers and 21 barriers (aggregated to 5 groups), being selected for each EET and country. The 5 groups considered to analyse drivers and barriers are environmental, technical, economic, social, legal. The perceived barriers and drivers were further studied for their association across the countries using the Pearson's Chi2 and a Cramer's V tests. The results demonstrate that across all EETs and countries, the technical and economic driver groups are perceived to have the highest potential to increase the implementation rate of EET. In terms of barriers, economic aspects are seen as the foremost reason that EET are not scaling faster. In both drivers and barriers legal aspects are the least often selected. In overall the barrier groups show significant variation across countries compared to driver groups. These findings provide an evidence-basis to better understand arguments in favour and against specific EETs and, in this way, support policy makers and other interested parties to increase the market share of the selected solutions.
|
|
| 3. |
- Han, Mengjie, 1985-, et al.
(author)
-
Generating hourly electricity demand data for large-scale single-family buildings by a decomposition-recombination method
- 2022
-
In: Energy and Built Environment. - : Elsevier BV. - 2666-1233.
-
Journal article (peer-reviewed)abstract
- Household electricity demand has substantial impacts on local grid operation, energy storage and the energy performance of buildings. Hourly demand data at district or urban level helps stakeholders understand the demand patterns from a granular time scale and provides robust evidence in energy management. However, such type of data is often expensive and time-consuming to collect, process and integrate. Decisions built upon smart meter data have to deal with challenges of privacy and security in the whole process. Incomplete data due to confidentiality concerns or system failure can further increase the difficulty of modeling and optimization. In addition, methods using historical data to make predictions can largely vary depending on data quality, local building environment, and dynamic factors. Considering these challenges, this paper proposes a statistical method to generate hourly electricity demand data for large-scale single-family buildings by decomposing time series data and recombining them into synthetics. The proposed method used public data to capture seasonality and the distribution of residuals that fulfill statistical characteristics. A reference building was used to provide empirical parameter settings and validations for the studied buildings. An illustrative case in a city of Sweden using only annual total demand was presented for deploying the proposed method. The results showed that the proposed method can mimic reality well and represent a high level of similarity to the real data. The average monthly error for the best month reached 15.9% and the best one was below 10% among 11 tested months. Less than 0.6% improper synthetic values were found in the studied region.
|
|
| 4. |
- Han, Mengjie, 1985-, et al.
(author)
-
The reinforcement learning method for occupant behavior in building control : A review
- 2021
-
In: Energy and Built Environment. - : Elsevier BV. - 2666-1233. ; 2:2, s. 137-148
-
Journal article (peer-reviewed)abstract
- Occupant behavior in buildings has been considered the major source of uncertainty for assessing energy consumption and building performance. Modeling frameworks are usually built to accomplish a certain task, but the stochasticity of the occupant makes it difficult to apply that experience to a similar but distinct environment. For complex and dynamic environments, the development of smart devices and computing power makes intelligent control methods for occupant behaviors more viable. It is expected that they will make a substantial contribution to reducing global energy consumption. Among these control techniques, the reinforcement learning (RL) method seems distinctive and applicable. The success of the reinforcement learning method in many artificial intelligence applications has given an explicit indication of how this method might be used to model and adjust occupant behavior in building control. Fruitful algorithms complement each other and guarantee the quality of the optimization. However, the examination of occupant behavior based on reinforcement learning methodologies is not well established. The way that occupant interacts with the RL agent is still unclear. This study briefly reviews the empirical applications using reinforcement learning, how they have contributed to shaping the modeling paradigms and how they might suggest a future research direction.
|
|
| 5. |
- Hawas, Allan, et al.
(author)
-
An Innovative Approach Towards Enhancing Energy Conservation in Buildings via Public Engagement Using DIY Infrared Thermography Surveys
- 2022
-
In: Energy and Built Environment. - : Elsevier BV. - 2666-1233. ; 3:1, s. 1-15
-
Journal article (peer-reviewed)abstract
- Energy consumption in urban environment in the EU accounts for about 40% of the total energy consumption, and the majority of this energy is utilised for heating and air conditioning of buildings. Hence the process of insulating and retrofitting of relatively old buildings is essential to enhance the thermal performance and hence contribute to energy and carbon emission reduction. There is a need to enhance people's engagement and education in relation to such issues to inspire and encourage positive actions and investment from the public. This paper presents an approach of combining a novel training process using a low-cost infrared thermal camera with small scale building model to promote DIY (Do-It-Yourself) infrared survey for the public to evaluate the performance of their own homes in order to identify any issues related to insulation or air leaks from the building envelop to encourage them to take corrective actions. The work included the engagement of 50 people to survey their own homes to capture the technical findings as well as their personal reaction and feedback. The results show that 88% of participants have found the educational session helpful to understand the infrared thermography; and 92% have considered the infrared camera to be an effective tool to indicate location of heat losses. Additionally, 90% of participants trust that the thermal camera has helped them to identify insulation defects that cause heat losses in their homes. Moreover, 84% believe that the thermal imaging has convinced them to think more seriously about the heat losses of their homes and what they could do to improve that. The experimental thermography surveys have shown that many houses have limitations in terms of thermal insulation which have been identified by the participants. This DIY interaction has provided enhanced public engagement and energy awareness via the use of the technology. The financial issues are also found to be critical, as none of the participants would have done the survey if they had to pay for it. Hence, this paper provides a solution for households with limited budgets.
|
|
