Investigating Smart City Development Based on Green Buildings, Electrical Vehicles and Feasible Indicators

With a goal of achieving net-zero emissions by developing Smart Cities (SCs) and industrial decarbonization, there is a growing desire to decarbonize the renewable energy sector by accelerating green buildings (GBs) construction, electric vehicles (EVs), and ensuring long-term stability, with the expectation that emissions will need to be reduced by at least two thirds by 2035 and by at least 90% by 2050. Implementing GBs in urban areas and encouraging the use of EVs are cornerstones of transition towards SCs, and practical actions that governments can consider to help with improving the environment and develop SCs. This paper investigates different aspects of smart cities development and introduces new feasible indicators related to GBs and EVs in designing SCs, presenting existing barriers to smart cities development, and solutions to overcome them. The results demonstrate that feasible and achievable policies such as the development of the zero-energy, attention to design parameters, implementation of effective indicators for GBs and EVs, implementing strategies to reduce the cost of production of EVs whilst maintaining good quality standards, load management, and integrating EVs successfully into the electricity system, are important in smart cities development. Therefore, strategies to governments should consider the full dynamics and potential of socio-economic and climate change by implementing new energy policies on increasing investment in EVs, and GBs development by considering energy, energy, techno-economic, and environmental benefits

Data for: A Comparative Assessment of Imputation Techniques on Measured Building Energy Data

Data in summary: 1- Building total B side: This is metered data from one of two mains busbars that supplies all none-emergency services and HVAC equipment 2- Building total A side: This is metered data from the second of two mains busbars that supplies all emergency services including fire safety, comm rooms, emergency lighting and public announcement. It also is connected to a PV array with peak electrical supply of around 33kWe. 3-Lighting: Half hourly lighting energy consumption dataset 4- Circulating Pumps: Pump 1 is significantly influenced by the occupants. Pump 2 is independent from occupancy influence. 5- AHU: Half hourly AHU dataset 6- Lift- Half hourly lift datase

Data for: A Comparative Assessment of Imputation Techniques on Measured Building Energy Data

Data in summary: 1- Building total B side: This is metered data from one of two mains busbars that supplies all none-emergency services and HVAC equipment 2- Building total A side: This is metered data from the second of two mains busbars that supplies all emergency services including fire safety, comm rooms, emergency lighting and public announcement. It also is connected to a PV array with peak electrical supply of around 33kWe. 3-Lighting: Half hourly lighting energy consumption dataset 4- Circulating Pumps: Pump 1 is significantly influenced by the occupants. Pump 2 is independent from occupancy influence. 5- AHU: Half hourly AHU dataset 6- Lift- Half hourly lift datase