Nowadays, the importance of energy management and optimization by means of smart devices has arisen as an important issue. On the other hand, the intelligent application of smart devices stands as a key element in establishing smart cities, which have been suggested as the solution to complicated future urbanization difficulties in coming years. Considering the scarcity of traditional fossil fuels in the near future, besides their ecological problems the new smart grids have demonstrated the potential to merge the non-renewable and renewable energy resources into each other leading to the reduction of environmental problems and optimizing operating costs. The current paper clarifies the importance of smart grids in launching smart cities by reviewing the advancement of micro/nano grids, applications of renewable energies, energy-storage technologies, smart water grids in smart cities. Additionally a review of the major European smart city projects has been carried out. These will offer a wider vision for researchers in the operation, monitoring, control and audit of smart-grid systems.

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Short-term load forecasting ensures the efficient operation of power systems besides affording continuous power supply for energy consumers. Smart meters that are capable of providing detailed information on buildings energy consumption, open several doors of opportunity to short-term load forecasting at the individual building level. In the current paper, four machine learning methods have been employed to forecast the daily peak and hourly energy consumption of domestic buildings. The utilized models depend merely on buildings historical energy consumption and are evaluated on the profiles that were not previously trained on. It is evident that developing data-driven models lacking external information such as weather and building data are of great importance under the situations that the access to such information is limited or the computational procedures are costly. Moreover, the performance evaluation of the models on separated house profiles determines their generalization ability for unseen consumption profiles. The conducted experiments on the smart meter data of several UK houses demonstrated that if the models are fed with sufficient historical data, they can be generalized to a satisfactory level and produce quite accurate results even if they only use past consumption values as the predictor variables. Furthermore, among the four applied models, the ones based on deep learning and ensemble techniques, display better performance in predicting daily peak load consumption than those of others.

Cities are becoming smarter by incorporating hardware technology, software systems, and network infrastructure that provide Information Technology (IT) systems with real-time awareness of the real world. What makes a “smart city” functional is the combined use of advanced infrastructure technologies to deliver its core services to the public in a remarkably efficient manner. City dashboards have drawn increasing interest from both city operators and citizens. Dashboards can gather, visualize, analyze, and inform regional performance to support the sustainable development of smart cities. They provide useful tools for evaluating and facilitating urban infrastructure components and services. This work proposes an interactive web-based data visualization and data analytics toolkit supported by big data aggregation tools. The system proposed is a cloud-based prototype that supports visualization and real-time monitoring of city trends while processing and displaying large data sets on a standard web browser. However, it is capable of supporting online analysis processing by answering analytical queries and producing graphics from multiple resources. The aim of this platform is to improve communication between users and urban service providers and to give citizens an overall view of the city’s state. The conceptual framework and architecture of the proposed platform are explored, highlighting design challenges and providing insight into the development of smart cities. Moreover, results and the potential statistical analysis of important city services offered by the system are introduced. Finally, we present some challenges and opportunities identified through the development of the city data platform.