The occupancy datasets are useful for planning important buildings’ related tasks such as optimal design, space utilization, energy management, maintenance, etc. Researchers are currently working on two key issues in building management systems. First, feasible and economical deployment of indoor and outdoor weather and energy monitoring sensors for data acquisition. Second, the development and implementation of cost-effective data-driven models with regular monitoring to ensure satisfactory performance for occupancy prediction. In this context, we present an occupancy forecasting model for different types of rooms in an academic building. A comprehensive dataset comprising indoor and outdoor environmental variables such as energy consumption, Heating, Ventilation, and Air Conditioning (HVAC) operational details and information on Wi-Fi-connected devices of a campus building, is used for occupants’ count prediction. A Light Gradient Boost Machine (LGBM) is applied for the selection of suitable features. After the feature selection, Machine Learning (ML) models such as Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), Long Short-Term Memory (LSTM) and Categorical Boosting (CatBoost) are employed to predict occupants’ count in each room. The models’ performances are evaluated using Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and Normalized Root Mean Square Error (NRMSE). The proposed LGBM-XgBoost model outperforms other approaches for each type of space. Moreover, to highlight the importance of LGBM as a feature selection technique, the XgBoost model is also trained with all features. Results indicate that by selecting the appropriate features through LGBM, the RMSE and MAE for lecture rooms 1 and 2 are improved by 61.67%, 36.17% and 67.05%, 63.67%, respectively. Similarly, for office rooms 1 and 2 RMSE and MAE are improved by 33.37%, 71.5% and 59.7%, 51.45%, respectively.

Smart Grid is a way of providing bidirectional energy flow with the integration of latest communication technologies and advanced control methods to overcome the issues associated with the current power grid such as unidirectional power flow, resource wastage, reliability, security, enhanced quality, and increasing demand of energy. Integrating Internet of Things (IoTs) makes Smart Grid hyperaware and agile to enhance the efficiency, reliability, and sustainability of electricity distribution. IoT‐enabled Smart Grids use IoT devices and sensors to collect real‐time data, allowing for automated monitoring, predictive maintenance, and better demand management. This study comprehensively reviews various wireless technologies for IoT‐enabled Smart Grids that could be integrated into home area networks (HANs), neighborhood area networks (NANs), and wide‐area networks (WANs). This work provides a comparison of wireless technologies for HANs, NANs, and WANs in terms of data rates, range, adaptability, internet protocol support, and various other parameters. IoT technologies, including ZigBee, Z‐wave, Bluetooth, Wi‐Fi, NFC, 6LoWPAN, wireless HART, and Thread, are suggested for operations in HANs according to the customer requirements with their operating characteristics. Similarly, for NANs and WANs, long‐term evolution (LTE), WiMax, and LPWAN are explored in terms of requirements of utilities. The study further analyzes the applications of IoT‐enabled Smart Grids and elaborates on the associated challenges and issues.

The integration of Variable Renewable Energy (VRE) sources in power systems is increased for a sustainable environment. However, due to the intermittent nature of VRE sources, formulating efficient economic dispatching strategies becomes challenging. This systematic review aims to elucidate the economic value creation of Artificial Intelligence (AI) in supporting the integration of VRE sources into power systems by reviewing the role of AI in mitigating costs related to balancing, profile, and grid with a focus on its applications for generation and demand forecasting, market design, demand response, storage solutions, power quality enhancement, and predictive maintenance. The proposed study evaluates the AI potential in economic efficiency and operational reliability improvement by analyzing the use cases with various Renewable Energy Resources (RERs), including wind, solar, geothermal, hydro, ocean, bioenergy, hydrogen, and hybrid systems. Furthermore, the study also highlights the development and limitations of AI-driven approaches in renewable energy sector. The findings of this review aim to highlight AI’s critical role in optimizing VRE integration, ultimately informing policymakers, researchers, and industry stakeholders about the potential of AI for an economically sustainable and resilient energy infrastructure.