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.

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.

In Pakistan, data for household electricity consumption are available in the form of monthly electricity bills only, and, therefore, are not helpful in establishing appliance-wise consumption. Further, it does not help in establishing the relationship among the household electricity consumption and various driving factors. This study aimed to unlock the household electricity consumption in Pakistan by analyzing electricity bills and investigating the impact of various socioeconomic, demographic, and dwelling parameters and usage of different appliances. The methodology adopted in this study was survey-based data collection of the residential sector. For this purpose, data were collected from 523 dwellings through surveys and interviews in Mirpur city. The results of the data analysis revealed that the average household electricity consumption is 2469 kWh/year with an average family size of seven and an average floor area of 78.91 m². Based on possession of various appliances, the households were categorized into four types and their consumption patterns were established and compared. Air Conditioned (AC) houses consume 44% more electricity compared to the non-AC houses, whereas an Uninterrupted Power Supply (UPS) consumes electricity equivalent to an AC. The research findings are useful for policy makers and building designers and are discussed in the conclusion section.

In an era defined by the relentless influx of data from diverse sources, the ability to harness and extract valuable insights from streaming data has become paramount. The rapidly evolving realm of online learning techniques is tailored specifically for the unique challenges posed by streaming data. As the digital world continues to generate vast torrents of real-time data, understanding and effectively utilizing online learning approaches are pivotal for staying ahead in various domains. One of the primary goals of online learning is to continuously update the model with the most recent data trends while maintaining and improving the accuracy of previous trends. Based on the various types of feedback, online learning tasks can be divided into three categories: learning with full feedback, learning with limited feedback, and learning without feedback. This survey aims to identify and analyze the key challenges associated with online learning with full feedback, including concept drift, catastrophic forgetting, skewed learning, and network adaptation, while the other existing reviews mainly focus on a single challenge or two without considering other scenarios. This article also discusses the application and ethical implications of online learning. The results of this survey provide valuable insights for researchers and instructional designers seeking to create effective online learning experiences that incorporate full feedback while addressing the associated challenges. In the end, some conclusions, remarks, and future directions for the research community are provided based on the findings of this review.

This study investigates the application of LiDAR point cloud datasets for building segmentation through a combined approach that integrates unsupervised segmentation with evolutionary optimization. The research evaluates the extent of improvement achievable through genetic algorithm (GA) optimization for LiDAR point cloud segmentation. The unsupervised methodology encompasses preprocessing, adaptive thresholding, morphological operations, contour filtering, and terrain ruggedness analysis. A genetic algorithm was employed to fine-tune the parameters for these techniques. Critical tunable parameters, such as the interpolation method for DSM and DTM generation, scale factor for contrast enhancement, adaptive constant and block size for adaptive thresholding, kernel size for morphological operations, squareness threshold to maintain the shape of predicted objects, and terrain ruggedness index (TRI) were systematically optimized. The study presents the top ten chromosomes with optimal parameter values, demonstrating substantial improvements of 29% in the average intersection over union (IoU) score (0.775) on test datasets. These findings offer valuable insights into LiDAR-based building segmentation, highlighting the potential for increased precision and effectiveness in future applications.