Quality in healthcare service is essential in giving rural dwellers a good standard of living. It has been established that many rural locations in Sub-Saharan Africa away from the grid connection have difficulty accessing electricity. The inaccessibility of reliable energy and essential medical equipment was the leading barrier to improved healthcare delivery in these rural locations. The deficiency of basic medical equipment to power essential services due to limited or unreliable electricity access has reduced rural healthcare workers’ care capabilities, resulting in higher mortality rates. This paper, therefore, reviews the existing energy solutions for rural healthcare facilities, thereby analysing different approaches and the geographical energy mix and ascertaining the effectiveness of various techniques and energy mix as solutions to effective healthcare delivery in healthcare centres. Hybrid Renewable Energy Sources (HRES) microsystems, like microgrids incorporated with solar panels and battery, is identified to ensure higher and more reliable energy access in rural healthcare centres. At the same time, the adoption of Demand Side Management (DSM) in the HRES deployment in countryside healthcare facilities is reported to decrease the initial cost of installation and improve efficiency. Lastly, in improving energy access, rural electrification planning is achieved through modelling tools related to energy access modelling.

Carbon dioxide (CO₂) and other greenhouse gases are the main causes of global climate change. This phenomenon impacts natural and human systems around the world through the rising global average surface temperature, extreme weather, changes in precipitation patterns, rising sea levels, and ocean acidification. However, this concept is alien to most people in developing countries. They are also unaware of the connection between energy efficiency and climate change. This dearth of knowledge makes them opt for highly inefficient appliances. Internet of Things (IoT)-based visualisation platforms for tracking household carbon footprints (CFs) have been seen as a good concept for combating this global phenomenon; however, there are potential challenges and ethical restrictions that must be addressed when implementing platforms for tracking household CFs. It is also vital to consider the user’s viewpoint and current technological state to ensure successful implementation and adoption. As the literature in this area is rapidly developing, it is crucial to revisit it occasionally. This paper presents a systematic review of IoT-based visualisation platforms for household CFs, including their definitions, characteristics, decision-making processes, policy development, related services, benefits, challenges, and barriers to implementation. Finally, it offers suggestions for future research.

This paper takes a cursory look at the problem of inadequate power supply in the rural healthcare centres of a developing country, specifically Nigeria, and proffers strategies to address this issue through the design of hybrid renewable energy systems combined with the existing unreliable grid in order to meet the healthcare load demand, thus ensuring higher reliability of available energy sources. The simulations, analysis and results presented in this paper are based on meteorological data and the load profiles of six selected locations in Nigeria, using which hybrid grid-connected systems integrating diesel, solar and wind energy sources are designed with configurations to give optimum output. The optimised design configurations in the considered case study, Ejioku, Okuru-Ama, Damare-Polo, Agbalaenyi, Kadassaka and Doso, produce very low energy costs of of 0.0791 $/kWh, 0.115 $/kWh, 0.0874 $/kWh, 0.0754 $/kWh, 0.0667 $/kWh and 0.0588 $/kWh, respectively, leveraging solar and wind energy sources which make higher percentage contributions at all sites. The load-following-dispatch strategy is adopted at all sites, ensuring that at every point in time, there is sufficient power to meet the needs of the healthcare centres. Further works on this topic could consider other strategies to optimise general energy usage on the demand side.

Blockchain technology, smart contract and microgrid systems have facilitated innovations and breakthroughs in the electricity industry. The once bundled electricity market dominated by a few key players is now gradually becoming unbundled to a more consumer-centric market due to these new technologies. To facilitate the use of community microgrids, this study develops a new trend in peer-to-peer energy trading. In this work, a model for a smart microgrid system, a decentralized energy trading platform based on blockchain, and smart contract technologies is proposed, considering an islanded community microgrid network of energy prosumers. Smart meters were used to ensure the bi-directional flow of data and power, thereby giving prosumers control over their power usage. Storage and validation of participants’ data are stored on the blockchain network, which has a strong feature of decentralization, transparency, security and data immutability. The smart contract automatically executes power delivery and transfer of tokens from the buyer’s wallet to the seller’s energy wallet based on the transaction logic and protocol. A web-user interface was designed to enable the ease of transactions by market participants and the web-user interface was designed on React.Javascript while the smart contract codes were done on the solidity programming language. Algorithms were also developed for the market trade operations in real time and sets of mathematical equations were formulated for energy pricing based on the supply and demand philosophies to curtail over-pricing and underpricing of energy.

This paper explores the use of deep learning to optimize the performance of a peer-to-peer energy system with an intelligent master controller. The goal addresses inefficiencies caused by energy seasonality by predicting hourly power consumption through a deep learning algorithm. The intelligent master controller was designed to manage the collaborative energy system, and the deep learning technique was employed as an optimization scheme to forecast power system performance for more efficient utilization. The deep learning algorithm was trained using dataset from American electric power, where consumer load data serves as input, and forecasted power serves as output. The forecasted power was then used as input to the intelligent master controller, which determines suitable power supply for generation and storage based on the predicted demand. The experiment results show promising accuracy with a root mean square error (RMSE) of 0.1819 for hourly energy consumption averaged over a year, 0.2419 for hourly energy consumption averaged over a month, 0.0662 for hourly energy consumption averaged per day, and 0.0217 for hourly energy consumption. These findings demonstrate that the system is well-trained and capable of accurately predicting the energy required by the intelligent master controller, thus enhancing the overall performance of the peer-to-peer energy system.