EAI Endorsed Transactions on Energy Web https://publications.eai.eu/index.php/ew <p>EAI Endorsed Transactions on Energy Web is an open access, peer-reviewed scholarly journal focused on cross-section topics related to IT and Energy. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a bi-monthly frequency (six issues per year). Authors are not charged for article submission and processing.</p> en-US <p>This is an open-access article distributed under the terms of the Creative Commons Attribution <a href="https://creativecommons.org/licenses/by/3.0/" target="_blank" rel="noopener">CC BY 3.0</a> license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.</p> publications@eai.eu (EAI Publications Department) support@eai.eu (EAI Support) Tue, 21 Jun 2022 12:39:29 +0000 OJS http://blogs.law.harvard.edu/tech/rss 60 Towards Multi-Model Big Data Road Traffic Forecast at Different Time Aggregations and Forecast Horizons https://publications.eai.eu/index.php/ew/article/view/1187 <p>Due to its usefulness in various social contexts, from Intelligent Transportation Systems (ITSs) to the reduction of urban pollution, road traffic prediction represents an active research area in the scientific community, with strong potential impact on citizens’ well-being. Already considered a non-trivial problem, in many real applications an additional level of complexity is given by the large amount of data requiring Big Data domain technologies. In this paper, we present the first steps of a novel approach integrating both classic and machine learning models in the Spark-based big data architecture of the H2020 CLASS project, and we perform preliminary tests to see how usually little-considered variables (different data aggregation levels, time horizons and traffic density levels) influence the error of the different models.</p> Riccardo Martoglia, Gabriele Savoia Copyright (c) 2022 EAI Endorsed Transactions on Energy Web https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/ew/article/view/1187 Wed, 25 May 2022 00:00:00 +0000 Sustainable Smart Parking Solution in a Campus Environment https://publications.eai.eu/index.php/ew/article/view/1191 <p>INTRODUCTION: With the continuous growth of cities and its demography, the number of vehicles has also increased in the cities which contributes to a greater difficulty in finding parking spaces. The time it takes for a citizen to find a free space in a car park can be tiring and contributes negatively to the level of air pollution. Smart Parking solutions intend to address this issue by proposing systems that, in many cases, include sensors and/or cameras with the purpose of facilitating the search for available parking spots.<br>OBJECTIVES: In this paper, a crowdsourcing-based approach that makes use of a mobile app for facilitating the search for a parking space in the Instituto Politécnico de Viana do Castelo is presented.<br>METHODS: The solutions intend to lower the time to park and, therefore, the amount of CO2 produced by vehicles of the academic community. Some gamification techniques were used to motivate users to be engaged with the mobile app.<br>RESULTS: A survey was used to evaluate the solution and the app usability. It showed that the use of the app can contribute to reduce the time spent to find a parking space in approximately 50.75%, and consequently reducing the CO2 by the same amount, and it was also verified that the users enjoyed using the mobile app.<br>CONCLUSION: The developed solution shows the efficient use of mobile applications, crowdsourcing and gamification approaches and their role to contribute to a more sustainable mobility.</p> Elsa Remelhe, Marcelo Cerqueira, Pedro Miguel Faria, Sara Paiva Copyright (c) 2022 EAI Endorsed Transactions on Energy Web https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/ew/article/view/1191 Wed, 25 May 2022 00:00:00 +0000 Sustainable Urban Mobility Boost Smart Toolbox Upgrade https://publications.eai.eu/index.php/ew/article/view/1193 <p>SUMBooST2 research develops universally applicable data science methodology which extracts key urban mobility parameters and origin/destination matrices from the anonymized big data set gathered from telecom operator. The methodology (toolbox) provides transport planners with a method for fast, efficient, and reliable provision of data on movements within the certain area. Origin/destination matrices with modal split will provide transport planners with valid input data for the planning of urban transport systems. The algorithms which separate relevant mobility data from the overall dataset are the unique part of the toolbox. The algorithms to identify passenger car trips are developed in 2020 project SUMBooST, and they are being upgraded in 2021 to detect trips made by active mobility modes and public transport. For the methodology to be valid, it must be implemented in representative number of cities. Previous SUMBooST project included implementation and validation in the City of Rijeka, and SUMBooST2 continues with two other cities, City of Zagreb, and City of Dubrovnik. The aim of the paper is to present innovative toolbox for the boost of sustainable urban planning based on big data science.</p> M. Sostaric, M. Jakovljevic, K. Vidovic, O. Lale Copyright (c) 2022 EAI Endorsed Transactions on Energy Web https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/ew/article/view/1193 Wed, 25 May 2022 00:00:00 +0000 Design of a low-cost wireless emission monitoring system for solid-fuel heat sources https://publications.eai.eu/index.php/ew/article/view/1369 <p>INTRODUCTION: The study presents a conceptual and detailed design methodology overview of a wireless sensor network (WSN) aimed to evaluate the SmartCity air quality in real-time. The aim of the article is to provide low-cost, reliable, and accurate emission data at remote locations, assisting the 2050 Climate-Neutral target set by the European Commission. The sensor devices shall monitor emission parameters such as ozone, carbon dioxide concentration and particle concentrations, specifically concentrations PM2,5 and PM10.<br>OBJECTIVES: The overall objective of the article is to demonstrate the use of a modern home-made air quality device within the market of boilers, fireplaces or other heating devices.<br>METHODS: The methodology consists of reverse engineering of used professional portable emission analyzers, determining the detailed parts, building a model of how the device's internal systems operate. The paper later explains a series of future experiments including the setup, the necessary components and specific aim. Two numerical models shall be constructed in MATLAB Simscape and Simulink, specifically a telecommunication model, assisting the design of the telecommunication module, and thermal numerical model, that aids in verifying the system temperature and cooling.<br>RESULTS: The study presents a large potential in increasing the accuracy and spectrum of modern air quality data and decreasing emission levels.<br>CONCLUSION: In the conclusion of the paper, the functional requirements of the data processing are stated, along with schematic illustration of the user interface of measured emissions.</p> M. Holubčík, Jozef Jandačka, M. Nicolanská Copyright (c) 2022 EAI Endorsed Transactions on Energy Web https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/ew/article/view/1369 Fri, 03 Jun 2022 00:00:00 +0000 Performance Optimization of Solar PV System Utilized for Cooling System https://publications.eai.eu/index.php/ew/article/view/1378 <p>This work investigates the performance and energy effectiveness of a solar photovoltaic (PV) system used to provide a cooling system for a building in Iraq. To achieve the goal, simulations and optimization are utilized to find the economic feasibility of the building in Iraq. In addition, a comparative study is conducted to compare the economic feasibility of PV cooling based on two options. The first option depends on the conventional electrical grid to offer cooling for the Iraqi building. The second option relies on a solar PV system to provide the electrical power for cooling the same building. The major numerical analysis results revealed that using a PV system can save roughly 45% electrical power compared to the option when the electrical power is drawn from the conventional grid. For this reason, it is predicted that the PV system can save a higher level of greenhouse gas (GHG) emissions compared to the first option. The results of this research revealed that the cooling load of the building in Samawah, Iraq equalled 600 kW. The PV system required to operate the cooling of the Samawah building during summer equals 18 kW peak. Using a solar PV system would be more economically feasible than the electrical power drawn from the electrical grid. Utilizing PV cooling is considered beneficial for the environment<br>as it can save GHG emissions that cause significant air quality problems and global warming.</p> Omar Hazem Mohammed, Ziyad Tariq Al-Salmany Copyright (c) 2022 EAI Endorsed Transactions on Energy Web https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/ew/article/view/1378 Wed, 08 Jun 2022 00:00:00 +0000 Task Scheduling Techniques for Energy Efficiency in the Cloud https://publications.eai.eu/index.php/ew/article/view/1509 <p>Energy efficiency is a key goal in cloud datacentre since it saves money and complies with green computing standards. When energy efficiency is taken into account, task scheduling becomes much more complicated and crucial. Execution overhead and scalability are major concerns in current research on energy-efficient task scheduling. Machine learning has been widely utilized to solve the problem of energy-efficient task scheduling, however, it is usually used to anticipate resource usage rather than selecting the schedule. The bulk of machine learning approaches are used to anticipate resource consumption, and heuristic or metaheuristic algorithms utilize these predictions to choose which computer resource should be assigned to a certain activity. As per the knowledge and research, none of the algorithms have independently used machine learning to make an energy-efficient scheduling decision. Heuristic or meta-heuristic approaches, as well as approximation algorithms, are frequently used to solve NP-complete problems. In this paper, we discuss various studies that have been used to solve the problem of task scheduling which belongs to a class of NP-hard. We have proposed a model to achieve the objective of reduced energy consumption and CO2 emission in a cloud environment. In the future, the model shall be implemented in MATLAB and would be assessed on various parameters like makespan, execution time, resource utilization, QoS, Energy utilization, etc.</p> Sanna Mehraj Kak, Parul Agarwal, M. Afshar Alam Copyright (c) 2022 EAI Endorsed Transactions on Energy Web https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/ew/article/view/1509 Mon, 20 Jun 2022 00:00:00 +0000 Linking Sustainable Mobility Criteria to Policymaking: Results of Multi-Criteria Analysis https://publications.eai.eu/index.php/ew/article/view/1549 <p>With increasing emissions from the transport sector, the need to reduce emissions is becoming increasingly acute. The EC's Climate Law aims to re-duce emissions by 55% by 2030, while the growing transport sector is the slowest to meet these targets. Only a few European Union (EU) countries met the 2020 renewable energy source target in the transport sector, which indicates that major changes are needed to meet the new EU requirements. As each country has limited financial resources, it is necessary to assess the impact of the policy before its implementation. In this study, a survey of 19 industry experts was conducted to identify the most promising policy in-struments for reducing emissions in the road transport sector, as well as to identify the most promising fuels for which more resources should be devoted. In this publication, data analysis was performed by the combined Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodology. The obtained data can be further used for in-depth analysis such as cost-benefit analysis or complex system dynamics analysis for later use in sustainable policy formulation.</p> Alina Safronova, Aiga Barisa, Vladimirs Kirsanovs Copyright (c) 2022 EAI Endorsed Transactions on Energy Web https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/ew/article/view/1549 Tue, 21 Jun 2022 00:00:00 +0000