Article
Engineering, Chemical
Enrique Rosales-Asensio, Ana-Esther Rosales, Antonio Colmenar-Santos
Summary: This article introduces the use of DesalinationPlant software to solve the nominal capacity calculation issue in a micro-grid, optimizing resources to maximize renewable micro-grid profits. The method is effective and efficient, providing optimal solutions within a limited timeframe.
Article
Chemistry, Multidisciplinary
Fei Lv, Zhixiao Qin, Jiazhe Wu, Lixia Pan, Longjie Liu, Yubin Chen, Yixin Zhao
Summary: Solar water splitting by photovoltaic electrolysis is a promising method for sustainable hydrogen production, but it currently requires multiple PV cells connected in series to meet the practical voltage requirements, which increases system complexity and resistance. By introducing a redox mediator, it is possible to construct a decoupled water electrolyzer that requires lower driving voltages, enabling single PV cell-driven water electrolysis.
Article
Green & Sustainable Science & Technology
D. Nikolic, J. Skerlic, J. Radulovic, A. Miskovic, R. Tamasauskas, J. Sadauskiene
Summary: In this paper, the authors investigate the optimal design of a Serbian residential building with photovoltaics and solar collectors on the roof, and analyze the performance of different heating systems. The results show that a positive-net energy building can be achieved with optimally sized photovoltaics and solar collectors, particularly in the case of a gas heating system. Environmental and economic analyses were also conducted to evaluate the most favorable solutions.
Article
Energy & Fuels
Tianyang Xia, Yiming Li, Zhouping Sun, Xiuchao Wan, Dapeng Sun, Lu Wang, Xingan Liu, Tianlai Li
Summary: The traditional design of the Chinese solar greenhouse (CSG) north wall cannot fully achieve thermal preservation and heat storage performance. Researchers have divided the greenhouse into independent solar heating system and independent north wall heat preservation system to improve thermal efficiency and solar energy utilization. A wall-mounted solar heating system using water as a medium for heat transport and storage has been developed. An active solar water curtain heating system with a non-woven fabric liner and black water-based paint coated polyethylene film was proposed to create a better suitable greenhouse thermal environment.
Article
Thermodynamics
Bo Xu, Tiantian Zhang, Siming Wang, Zhenqian Chen
Summary: This paper proposes a solar seasonal thermal energy storage system for a single-family dwelling, which uses assisted water source heat pump to improve system stability. A dynamic simulation model is established to analyze the energy transfer and conversion laws and dynamic operation characteristics of the system. The results show that the composite system improves energy efficiency, achieves cost savings, and reduces carbon dioxide emissions.
APPLIED THERMAL ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Gideon Oyibo, Thomas Barrett, Sharadh Jois, Jeffrey L. Blackburn, Ji Ung Lee
Summary: This study presents an all-semiconducting single-walled carbon nanotube device with a tandem geometry that models the process of photosynthesis. By separating the light absorption and power generation processes using distinct chirality and bandgap single-walled carbon nanotubes, the device captures photons from multiple regions of the solar spectrum and enhances the photoresponse.
Article
Computer Science, Interdisciplinary Applications
Sean Mann, Eric Fadel, Samuel S. Schoenholz, Ekin D. Cubuk, Steven G. Johnson, Giuseppe Romano
Summary: Partial derivative PV is an end-to-end differentiable photovoltaic cell simulator that efficiently computes the power conversion efficiency and its derivative with respect to input parameters. It has applications in perovskite solar cell optimization and multi-parameter discovery.
COMPUTER PHYSICS COMMUNICATIONS
(2022)
Article
Construction & Building Technology
Wenping Du, Ming Li, Yunfeng Wang, Xun Ma, Chengzhi Hu, Ying Zhang, Zhuoli Zhang
Summary: A novel method for constructing a distributed solar photovoltaic direct-drive cold storage system is proposed, where the key factors affecting system performance and energy efficiency are studied in detail. Experimental results show that the solar-cold energy conversion efficiency of the system is negatively correlated with solar radiation intensity and varies under different weather conditions.
BUILDING AND ENVIRONMENT
(2021)
Article
Construction & Building Technology
Wei Zhong, Shaoxiong Liu, Xiaojie Lin, Yi Zhou
Summary: This study proposes an optimized design of district heating systems (DHS) based on granularity analysis methodology to harness distributed solar energy. The study applies hierarchical clustering and pipeline sizing optimization to calculate the optimal scheme with the best economic performance. It also introduces a quantitative granularity estimation method. The research highlights the importance of proper selection of granularity in the design stage for the economic and stable operation of DHS.
JOURNAL OF BUILDING ENGINEERING
(2022)
Article
Energy & Fuels
Loiy Al-Ghussain, Onur Taylan, Mohammad Abujubbeh, Muhammed A. Hassan
Summary: To address the increasing installation capacities of solar PV systems in desert areas, this study examines the impacts of ambient temperature, wind speed, dust accumulation, and cleaning frequency on energy production and optimal angles of PV panels. Results show higher energy production estimates using an isotropic model, and the cooling effect of wind speed reduces the operating temperature of thin-film panels. Cleaning the panels bi-monthly decreases annual energy production, and doubling the dust accumulation rate decreases energy production for all cases. The optimal tilt and azimuth angles vary within -3.0 degrees with dust accumulation rate.
Article
Thermodynamics
Yan Zhang, Kai Han, Yongzhen Wang, Wenjie Ji, Lanlan Zhang, Wei Zhang
Summary: Building sector accounts for approximately 37% of global energy-related CO2 emissions in 2020, and building heating consumes 30% of energy used in buildings. This paper proposes a low-carbon building heating system coupled with a new semiconductor radiation heating unit and distributed rooftop photovoltaic to reduce carbon emissions. A dynamic model based on semiconductor low-temperature radiant heating is established to analyze the heat transfer, and the uncertainty from distributed rooftop photovoltaic and building heating demand is considered in the operation strategy. Simulation models for different climate zones in China show significant reduction in carbon emissions and cost savings compared to traditional grid-powered heating systems.
BUILDING SIMULATION
(2023)
Article
Energy & Fuels
Weiwei Xu, Huiqing Guo, Chengwei Ma
Summary: The addition of an active solar water wall and an underground water storage tank to the north wall of Chinese solar greenhouses can increase nighttime temperature. The water wall shows good performance in terms of heat collection and release capability, as well as thermal performance in adverse weather. The retrofitting of water walls can make warm-season crop production feasible throughout winter by eliminating supplemental heating.
Article
Energy & Fuels
Nelson Sommerfeldt, Joshua M. Pearce
Summary: This study quantifies the techno-economic potential of using solar photovoltaics (PV) to support heat pumps (HP) for replacing natural gas heating in North American residences. Simulations compare different heating systems, including PV with grid electricity and HP with grid electricity, to identify conditions for lower life cycle cost. The results show that PV becomes a hedge against rising prices when inflation rates are high or PV capital costs are low, increasing the adoption of HPs and reducing carbon emissions. The study also discusses the impact on energy policy, such as rebates and utility business models.
Article
Energy & Fuels
S. Jaanaa Rubavathy, Nithiyananthan Kannan, D. Dhanya, Santaji Krishna Shinde, N. B. Soni, Abhishek Madduri, V. Mohanavel, M. Sudhakar, Ravishankar Sathyamurthy
Summary: By utilizing renewable energies such as wind and solar energy, the structure of the energy system can be adjusted to address energy and environmental challenges. However, wind and solar energy generation are inherently unreliable due to their impact on the environment. This paper develops a KNN classification model to optimize the management and storage of energy, with a focus on lithium battery technology.
Article
Energy & Fuels
Shakti Singh, NirbhowJap Singh, Aditi Gupta
Summary: Hydrogen-based power generation, combined with solar PV, has emerged as an alternative energy source. A hybrid system of solar, fuel cell, battery, electrolyzer, and hydrogen tank has been developed to meet the electricity demand of a small academic complex. The economic analysis and mathematical modeling show the system's efficacy.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Thermodynamics
Shohei Miyata, Jongyeon Lim, Yasunori Akashi, Yasuhiro Kuwahara, Katsuhiko Tanaka
SCIENCE AND TECHNOLOGY FOR THE BUILT ENVIRONMENT
(2020)
Article
Environmental Sciences
Jieun Lee, Yasunori Akashi, Hiroto Takaguchi, Daisuke Sumiyoshi, Jongyeon Lim, Takahiro Ueno, Kento Maruyama, Yoshiki Baba
Summary: Developed a forecasting model based on System Dynamics to predict CO2 emissions, supporting policy-making and emission reduction actions in various cities.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2021)
Article
Green & Sustainable Science & Technology
Yuan Gao, Shohei Miyata, Yasunori Akashi
Summary: With the rapid development of computer technology, deep learning models are increasingly used in solar radiation prediction. In this study, a deep generative model based on LSTM is developed for multi-step solar irradiation prediction. The results show that the generative model can effectively avoid error accumulation and the introduction of temperature forecast data from the previous day can significantly improve the accuracy of the prediction.
Article
Construction & Building Technology
Wei-An Chen, Jongyeon Lim, Shohei Miyata, Yasunori Akashi
Summary: This study aims to develop a methodology for evaluating the potential of sewage heat utilization based on an urban sewage state prediction model. The constructed prediction model is applied to actual areas to assess the sewage heat utilization potential, without the need for measuring sewage flow rate and temperature data.
SUSTAINABLE CITIES AND SOCIETY
(2022)
Article
Construction & Building Technology
Jongyeon Lim, Wonjun Choi, Yasunori Akashi, Naoki Yoshimoto, Ryozo Ooka
Summary: This study proposes a data-driven probabilistic model to predict the group-level thermal satisfaction of occupants in a given thermal condition. The model considers the heterogeneity of individual variations in thermal sensation votes (TSVs) and treats TSVs as ordinal variables. Model parameters are estimated using Bayesian inference technique and the model's effectiveness is validated using a subset of ASHRAE Global Thermal Comfort Database II.
JOURNAL OF BUILDING ENGINEERING
(2022)
Article
Energy & Fuels
Yuan Gao, Shohei Miyata, Yasunori Akashi
Summary: With the rapid development of high-performance computing technology, data-driven models, especially deep learning models, are increasingly used for solar radiation prediction. However, the lack of interpretability in the black box models limits their application in final optimization scenarios. In this study, we proposed models based on recurrent neural network prediction model to improve the interpretability of the models through the design and improvement of the model structure, thereby increasing the credibility of the model results. The use of attention mechanism and graph neural network helped to study the interpretability in time and spatial dependencies of the prediction process. Results showed that the deep learning model, with attention, could effectively adapt to varying prediction target hours, and the graph neural network identified the most relevant variables related to solar radiation.
Article
Energy & Fuels
Yuan Gao, Yuki Matsunami, Shohei Miyata, Yasunori Akashi
Summary: This study focuses on the off-grid operation and battery safety optimization of a renewable building energy system using reinforcement learning algorithms. Through training and validation with real measured data, the proposed reinforcement learning design achieves the optimization goals of off-grid operation and battery safety.
Article
Energy & Fuels
Yuan Gao, Yuki Matsunami, Shohei Miyata, Yasunori Akashi
Summary: This paper proposes a multi-agent deep reinforcement learning algorithm (MADRL) to solve the reinforcement learning problem with hybrid action spaces in building controls. The algorithm is validated on a dataset from a real office building and shows significant improvements in off-grid operation tasks and battery safety.
Article
Construction & Building Technology
Yuan Gao, Yuki Matsunami, Shohei Miyata, Yasunori Akashi
Summary: This study proposes a hybrid prediction model and MPC framework to optimize the combination of solar PV and batteries in building energy systems. Validated with real building data, the results show significant improvements in battery safety and combined heat and power operation using the proposed framework.
SUSTAINABLE CITIES AND SOCIETY
(2023)
Article
Construction & Building Technology
Yuan Gao, Shohei Miyata, Yuki Matsunami, Yasunori Akashi
Summary: This study investigates the use of a transformer model to improve solar radiation prediction, achieving better accuracy by considering factors such as humidity and temperature. Detailed case studies and one-step analysis results confirm the importance of these factors in the transformer model's performance.
ENERGY AND BUILDINGS
(2023)
Article
Construction & Building Technology
Shohei Miyata, Jongyeon Lim, Yasunori Akashi, Yasuhiro Kuwahara
ADVANCES IN BUILDING ENERGY RESEARCH
(2020)
Proceedings Paper
Green & Sustainable Science & Technology
A. Motomura, S. Miyata, S. Adachi, Y. Akashi, J. Lim, K. Tanaka, Y. Kuwahara
SUSTAINABLE BUILT ENVIRONMENT CONFERENCE 2019 TOKYO (SBE19TOKYO) - BUILT ENVIRONMENT IN AN ERA OF CLIMATE CHANGE: HOW CAN CITIES AND BUILDINGS ADAPT?
(2019)
Proceedings Paper
Green & Sustainable Science & Technology
Y. Ohto, Y. Akashi, J. Lim
SUSTAINABLE BUILT ENVIRONMENT CONFERENCE 2019 TOKYO (SBE19TOKYO) - BUILT ENVIRONMENT IN AN ERA OF CLIMATE CHANGE: HOW CAN CITIES AND BUILDINGS ADAPT?
(2019)
Proceedings Paper
Green & Sustainable Science & Technology
K. Ojima, Y. Akashi, J. Lim, N. Yoshimoto, J. Chen
SUSTAINABLE BUILT ENVIRONMENT CONFERENCE 2019 TOKYO (SBE19TOKYO) - BUILT ENVIRONMENT IN AN ERA OF CLIMATE CHANGE: HOW CAN CITIES AND BUILDINGS ADAPT?
(2019)
Proceedings Paper
Green & Sustainable Science & Technology
Y. Inoshita, D. Sumiyoshi, Y. Akashi, H. Kitora
SUSTAINABLE BUILT ENVIRONMENT CONFERENCE 2019 TOKYO (SBE19TOKYO) - BUILT ENVIRONMENT IN AN ERA OF CLIMATE CHANGE: HOW CAN CITIES AND BUILDINGS ADAPT?
(2019)
Article
Construction & Building Technology
Samiran Khorat, Debashish Das, Rupali Khatun, Sk Mohammad Aziz, Prashant Anand, Ansar Khan, Mattheos Santamouris, Dev Niyogi
Summary: Cool roofs can effectively mitigate heatwave-induced excess heat and enhance thermal comfort in urban areas. Implementing cool roofs can significantly improve urban meteorology and thermal comfort, reducing energy flux and heat stress.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Qi Li, Jiayu Chen, Xiaowei Luo
Summary: This study focuses on the vertical wind conditions as a main external factor that limits the energy assessment of high-rise buildings in urban areas. Traditional tools for energy assessment of buildings use a universal vertical wind profile estimation, without taking into account the unique wind speed in each direction induced by the various shapes and configurations of buildings in cities. To address this limitation, the study developed an omnidirectional urban vertical wind speed estimation method using direction-dependent building morphologies and machine learning algorithms.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Xiaojun Luo, Lamine Mahdjoubi
Summary: This paper presents an integrated blockchain and machine learning-based energy management framework for multiple forms of energy allocation and transmission among multiple domestic buildings. Machine learning is used to predict energy generation and consumption patterns, and the proposed framework establishes optimal and automated energy allocation through peer-to-peer energy transactions. The approach contributes to the reduction of greenhouse gas emissions and enhances environmental sustainability.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Ying Yu, Yuanwei Xiao, Jinshuai Chou, Xingyu Wang, Liu Yang
Summary: This study proposes a dual-layer optimization design method to maximize the energy sharing potential, enhance collaborative benefits, and reduce the storage capacity of building clusters. Case studies show that the proposed design significantly improves the performance of building clusters, reduces energy storage capacity, and shortens the payback period.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Felix Langner, Weimin Wang, Moritz Frahm, Veit Hagenmeyer
Summary: This paper compares two main approaches to consider uncertainties in model predictive control (MPC) for buildings: robust and stochastic MPC. The results show that compared to a deterministic MPC, the robust MPC increases the electricity cost while providing complete temperature constraint satisfaction, while the stochastic MPC slightly increases the electricity cost but fulfills the thermal comfort requirements.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Somil Yadav, Caroline Hachem-Vermette
Summary: This study proposes a mathematical model to evaluate the performance of a Double Skin Facade (DSF) system and its impact on indoor conditions. The model considers various design parameters and analyzes their effects on the system's electrical output and room temperature.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Ruijun Chen, Holly Samuelson, Yukai Zou, Xianghan Zheng, Yifan Cao
Summary: This research introduces an innovative resilient design framework that optimizes building performance by considering a holistic life cycle perspective and accounting for climate projection uncertainties. The study finds that future climate scenarios significantly impact building life cycle performance, with wall U-value, windows U-value, and wall density being major factors. By using ensemble learning and optimization algorithms, predictions for carbon emissions, cost, and indoor discomfort hours can be made, and the best resilient design scheme can be selected. Applying this framework leads to significant improvements in building life cycle performance.
ENERGY AND BUILDINGS
(2024)
