In the era of digital data proliferation, agriculture stands on the cusp of a transformative revolution driven by Machine Learning (ML). This study delves into the intricate interplay between Information and Communications Technology (ICT) and conventional agriculture, emphasizing the role of ML in reshaping farming practices. With the ongoing data tsunami impacting data-driven businesses, the fusion of smart farming and precision agriculture emerges as a beacon of innovation. ML algorithms, analyzing historical and real-time environmental data, soil conditioning, predicts suitable crop for maximum yields, detect diseases, and optimize irrigation in smart farming, facilitating informed decision-making. Precision agriculture benefits from autonomous vehicles and drones, driven by ML, ensuring precision in planting, harvesting, and crop monitoring. Resource efficiency increases as ML optimizes energy consumption, manages fertilizer application, and promotes climate-resilient practices. This comprehensive assessment underscores ML’s pivotal role in maximizing productivity, minimizing environmental impact, and navigating the complexities of modern agriculture.

Demand Response (DR) has gained popularity in recent years as a practical strategy to increase the sustainability of energy systems while reducing associated costs. Despite this, Artificial Intelligence (AI) and Machine Learning (ML), have recently developed as critical technologies for demand-side management and response due to the high complexity of tasks associated with DR, as well as huge amount of data management to take decisions very near to real time implications. Selecting the best group of users to respond, learning their attitude toward consumptions and their priorities, price optimization, monitoring and control of devices, learning to engage more and more consumers in the DR schemes, and learning how to remunerate them fairly and economically are all problems that can be tackled with the help of AI techniques. This study presents an overview of AI approaches used for DR applications. Both the Artificial Intelligence and Machine Learning algorithm(s) are employed while discussing commercial efforts (from both new and existing businesses) and large-scale innovation projects that have applied AI technologies for energy DR. Different kind of DR programs implemented in different countries are also discussed. Moreover, it also discusses the application of blockchain for DR schemes in smart grid paradigm. Discussion of the strengths and weaknesses of the evaluated AI methods for various DR tasks, as well as suggestions for further study, round out the work.

Smart grid integrates computer, communication, and sensing technologies into existing power grid networks to achieve significant informatization-related advantages. It will provide communication between neighbors, localized management, bidirectional power transfer, and effective demand response. Smart grids (SG) replace conventional grids by integrating various operational measures, including smart automation appliances, smart meters, and renewable energy sources. Regarding energy management and resolving energy issues, SG is one of the most cutting-edge and potentially game-changing innovations. Even still, its complexity suggests that decentralization may provide significant gains. Because of its increasing digitization and interconnectedness, it is also vulnerable to cyber threats. Blockchain, in this sense, is a potential SG paradigm solution that provides several great benefits. Even though blockchains have been widely discussed to decentralize and strengthen smart grid cybersecurity, they have not yet been researched in depth from an application and architectural standpoint. Blockchain-enabled SG applications are the subject of an in-depth research investigation. Electric vehicles (EVs), home automation, energy management systems, etc., are only a few of the many examples that have prompted the proposal of blockchain designs for their respective use cases. Information communication network security is of paramount importance. However, this evolving system raises cybersecurity issues. This paper aims to guide researchers in the right manner so they may build blockchain-based, secure, distributed SG applications in the future. This article also summarizes cybersecurity threats pertaining to smart grids. It starts with a description of a blockchain followed by the blockchain infrastructure, challenges, and solutions for different smart grid applications. A look back at the tried-and-true methods of securing a power grid is offered, and then it discusses the newer and more complex cybersecurity threats to the smart grid. In addition, models of common cyberattacks are presented, and the methods of defense against them are examined.

Increases in population and industrialization have caused the consumption of electrical energy to perpetually increase. Therefore, it has become necessary to monitor, control, and efficiently utilize the energy consumption. Present power systems cannot monitor and control the flow of power at the distribution level efficiently; because of this, financial losses occur. Therefore, a system should be developed which can monitor and control the power system faster and in an efficient way using modern technologies in order to avoid these losses. This paper presents an IOT-based concept for energy management to monitor and control specific loads in buildings. Among many wireless technology protocols, the proposed solution uses Wi-Fi along with the programmable ESP8266 controller in order to monitor the voltage, current, and power of a few home appliances. The IOT scheme also pursues an additional web-server-based monitoring approach for the online visualization, monitoring, and control of electrical parameters.

Reliable and sustainable energy is fundamental to socio‐economic development; however, Pakistan faces persistent energy challenges due to rising demand and heavy reliance on costly and environmentally harmful fossil fuels, particularly in Balochistan. Given Gwadar's abundant solar irradiation and wind resources, this study evaluates the feasibility of achieving a net‐zero energy city through three renewable‐based power system configurations using solar, wind, and battery storage. Model 1, relying solely on solar photovoltaic (PV) cells and batteries, yields a levelised cost of energy (LCOE) of $0.441/kWh, with a total net present cost (NPC) of $182 million. Model 2, integrating solar, wind turbines, and batteries, reduces the LCOE to $0.320/kWh, with an NPC of $133 million. Model 3 incorporates solar, wind, and a diesel generator, offering the lowest LCOE of $0.219/kWh and an NPC of $90.7 million. While Model 1 is the most environmentally sustainable, it has the highest LCOE and NPC. Model 3 ensures the highest reliability with the lowest LCOE and NPC at the expense of environmental impact due to diesel. The payback rate for Model 1 is 6.2 years; for Model 2, it is 7.79 years; and Model 3 has the lowest payback rate of 4.62 years. This study provides a comprehensive techno‐economic analysis, identifying optimal solutions for Gwadar's energy requirements, which can be replicated in similar off‐grid areas.

Presents corrections to the paper, Evaluation of Machine Learning Approaches for Precision Farming in Smart Agriculture System: A Comprehensive Review.