Comparative Analysis of Web of Science and Scopus on the Energy Efficiency and Climate Impact of Buildings

Although the body of scientific publications on energy efficiency and climate mitigation from buildings has been growing quickly in recent years, very few previous bibliometric analysis studies exist that analyze the literature in terms of specific content (trends or options for zero‐energy buildings) or coverage of different scientific databases. We evaluate the scientific literature published since January 2013 concerning alternative methods for improving the energy efficiency and mitigating climate impacts from buildings. We quantify and describe the literature through a bibliometric approach, comparing the databases Web of Science (WoS) and Scopus. A total of 19,416 (Scopus) and 17,468 (WoS) publications are analyzed, with only 11% common documents. The literature has grown steadily during this time period, with a peak in the year 2017. Most of the publications are in English, in the area of Engineering and Energy Fuels, and from institutions from China and the USA. Strong links are observed between the most published authors and institutions worldwide. An analysis of keywords reveals that most of research focuses on technologies for heating, ventilation, and air‐conditioning, phase change materials, as well as information and communication technologies. A significantly smaller segment of the literature takes a broader perspective (greenhouse gas emissions, life cycle, and sustainable development), investigating implementation issues (policies and costs) or renewable energy (solar). Knowledge gaps are detected in the areas of behavioral changes, the circular economy, and some renewable energy sources (geothermal, biomass, small wind). We conclude that i) the contents of WoS and Scopus are radically different in the studied fields; ii) research seems to focus on technological aspects; and iii) there are weak links between research on energy and on climate mitigation and sustainability, the latter themes being misrepresented in the literature. These conclusions should be validated with further analyses of the documents identified in this study. We recommend that future research focuses on filling the above identified gaps, assessing the contents of several scientific databases, and extending energy analyses to their effects in terms of mitigation potentials.This work was funded by the Ministerio de Ciencia, Innovación y Universidades de España (RTI2018‐ 093849‐B‐C31), by ICREA under the ICREA Academia programme, and by the foundation SIVL

Internal report cluster 1: Urban freight innovations and solutions for sustainable deliveries (1/4)

Technical report about sustainable urban freight solutions, part 1 of

Learning Agent for a Heat-Pump Thermostat With a Set-Back Strategy Using Model-Free Reinforcement Learning

The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant during the day. This constant temperature set point ensures that the heat pump operates in its more efficient heat-pump mode and minimizes the risk of activating the less efficient auxiliary heating element. As an alternative to a constant set-point strategy, this paper proposes a learning agent for a thermostat with a set-back strategy. This set-back strategy relaxes the set-point temperature during convenient moments, e.g. when the occupants are not at home. Finding an optimal set-back strategy requires solving a sequential decision-making process under uncertainty, which presents two challenges. A first challenge is that for most residential buildings a description of the thermal characteristics of the building is unavailable and challenging to obtain. A second challenge is that the relevant information on the state, i.e. the building envelope, cannot be measured by the learning agent. In order to overcome these two challenges, our paper proposes an auto-encoder coupled with a batch reinforcement learning technique. The proposed approach is validated for two building types with different thermal characteristics for heating in the winter and cooling in the summer. The simulation results indicate that the proposed learning agent can reduce the energy consumption by 4-9% during 100 winter days and by 9-11% during 80 summer days compared to the conventional constant set-point strategyComment: Submitted to Energies - MDPI.co

Energy Efficiency and Emission Testing for Connected and Automated Vehicles Using Real-World Driving Data

By using the onboard sensing and external connectivity technology, connected and automated vehicles (CAV) could lead to improved energy efficiency, better routing, and lower traffic congestion. With the rapid development of the technology and adaptation of CAV, it is more critical to develop the universal evaluation method and the testing standard which could evaluate the impacts on energy consumption and environmental pollution of CAV fairly, especially under the various traffic conditions. In this paper, we proposed a new method and framework to evaluate the energy efficiency and emission of the vehicle based on the unsupervised learning methods. Both the real-world driving data of the evaluated vehicle and the large naturalistic driving dataset are used to perform the driving primitive analysis and coupling. Then the linear weighted estimation method could be used to calculate the testing result of the evaluated vehicle. The results show that this method can successfully identify the typical driving primitives. The couples of the driving primitives from the evaluated vehicle and the typical driving primitives from the large real-world driving dataset coincide with each other very well. This new method could enhance the standard development of the energy efficiency and emission testing of CAV and other off-cycle credits

Final Report: Market and Economic Modelling of the Intelligent Grid

The overall goal of Project 2 has been to provide a comprehensive understanding of the impacts of distributed energy (DG) on the Australian Electricity System. The research team at the UQ Energy Economics and Management Group (EEMG) has constructed a variety of sophisticated models to analyse the various impacts of significant increases in DG. These models stress that the spatial configuration of the grid really matters - this has tended to be neglected in economic discussions of the costs of DG relative to conventional, centralized power generation. The modelling also makes it clear that efficient storage systems will often be critical in solving transient stability problems on the grid as we move to the greater provision of renewable DG. We show that DG can help to defer of transmission investments in certain conditions. The existing grid structure was constructed with different priorities in mind and we show that its replacement can come at a prohibitive cost unless the capability of the local grid to accommodate DG is assessed very carefully.Distributed Generation. Energy Economics, Electricity Markets, Renewable Energy

Decomposition of a greenhouse TS-Fuzzy model by clustering process

This paper presents a fuzzy c-means clustering method for decompose a T-S fuzzy system. This technique is used to organize the fuzzy greenhouse climate model into a new structure more interpretable, as in the case of the physical model. This new methodology was tested to split the inside greenhouse air temperature and humidity flat fuzzy models into fuzzy sub-models. These fuzzy sub-models are compared with its counterpart’s physical sub-models. This algorithm is applied to the T-S fuzzy rules. The results are several clusters of rules where each cluster is a new fuzzy sub-system. This is a generalized Probabilistic Fuzzy C-Means (PFCM) algorithm applied to TS-Fuzzy System clustering. This allows automatic organization of one fuzzy system into a multimodel Hierarchical Structure.This work was supported by Fundação para a Ciência e Tecnologia (FCT) under grant POSI/SRI/41975/2001 and by CITAB - Centro de Investigação e Tecnologias Agro-Abientais e Biológicas