Farghali Mohamed, Osman Ahmed I, Mohamed Israa M A, Chen Zhonghao, Chen Lin, Ihara Ikko, Yap Pow-Seng, Rooney David W
Department of Agricultural Engineering and Socio-Economics, Kobe University, Kobe, 657-8501 Japan.
Department of Animal and Poultry Hygiene and Environmental Sanitation, Faculty of Veterinary Medicine, Assiut University, Assiut, 71526 Egypt.
Environ Chem Lett. 2023 Mar 23:1-37. doi: 10.1007/s10311-023-01591-5.
New technologies, systems, societal organization and policies for energy saving are urgently needed in the context of accelerated climate change, the Ukraine conflict and the past coronavirus disease 2019 pandemic. For instance, concerns about market and policy responses that could lead to new lock-ins, such as investing in liquefied natural gas infrastructure and using all available fossil fuels to compensate for Russian gas supply cuts, may hinder decarbonization efforts. Here we review energy-saving solutions with a focus on the actual energy crisis, green alternatives to fossil fuel heating, energy saving in buildings and transportation, artificial intelligence for sustainable energy, and implications for the environment and society. Green alternatives include biomass boilers and stoves, hybrid heat pumps, geothermal heating, solar thermal systems, solar photovoltaics systems into electric boilers, compressed natural gas and hydrogen. We also detail case studies in Germany which is planning a 100% renewable energy switch by 2050 and developing the storage of compressed air in China, with emphasis on technical and economic aspects. The global energy consumption in 2020 was 30.01% for the industry, 26.18% for transport, and 22.08% for residential sectors. 10-40% of energy consumption can be reduced using renewable energy sources, passive design strategies, smart grid analytics, energy-efficient building systems, and intelligent energy monitoring. Electric vehicles offer the highest cost-per-kilometer reduction of 75% and the lowest energy loss of 33%, yet battery-related issues, cost, and weight are challenging. 5-30% of energy can be saved using automated and networked vehicles. Artificial intelligence shows a huge potential in energy saving by improving weather forecasting and machine maintenance and enabling connectivity across homes, workplaces, and transportation. For instance, 18.97-42.60% of energy consumption can be reduced in buildings through deep neural networking. In the electricity sector, artificial intelligence can automate power generation, distribution, and transmission operations, balance the grid without human intervention, enable lightning-speed trading and arbitrage decisions at scale, and eliminate the need for manual adjustments by end-users.
在气候变化加速、俄乌冲突以及过去的2019冠状病毒病大流行的背景下，迫切需要节能的新技术、系统、社会组织和政策。例如，对可能导致新的锁定效应的市场和政策反应的担忧，如投资液化天然气基础设施以及使用所有可用化石燃料来弥补俄罗斯天然气供应削减，可能会阻碍脱碳努力。在此，我们审视节能解决方案，重点关注实际能源危机、化石燃料供暖的绿色替代方案、建筑和交通领域的节能、可持续能源领域的人工智能以及对环境和社会的影响。绿色替代方案包括生物质锅炉和炉灶、混合热泵、地热供暖、太阳能热系统、太阳能光伏系统转化为电锅炉、压缩天然气和氢气。我们还详细介绍了德国的案例研究，德国计划到2050年实现100%可再生能源转型，并在中国发展压缩空气储能，重点关注技术和经济方面。2020年全球能源消费中，工业占30.01%，交通占26.18%，住宅部门占22.08%。使用可再生能源、被动式设计策略、智能电网分析、节能建筑系统和智能能源监测，可将能源消耗降低10%至40%。电动汽车每公里成本降低幅度最高可达75%，能源损失最低可达33%，但与电池相关的问题、成本和重量仍具有挑战性。使用自动化和联网车辆可节省5%至30%的能源。人工智能在节能方面具有巨大潜力，可改善天气预报和机器维护，并实现家庭、工作场所和交通之间的互联互通。例如，通过深度神经网络可将建筑物的能源消耗降低18.97%至42.60%。在电力部门，人工智能可实现发电、配电和输电操作自动化，无需人工干预即可平衡电网，实现大规模闪电式交易和套利决策，并消除终端用户进行人工调整的需求。
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