• Energy Research

This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.

In the past few years, the internet of things (IoT) has garnered a lot of attention owing to its significant deployment for fulfilling the global demand. It has been seen that power-efficient devices such as sensors and IoT play a significant role in our regular lives. However, the popularity of IoT sensors and low-power electronic devices is limited due to the lower lifetime of various energy resources which are needed for powering the sensors over time. For overcoming this issue, it is important to design and develop better, high-performing, and effective energy harvesting systems. In this article, different types of ambient energy harvesting systems which can power IoT-enabled sensors, as well as wireless sensor networks (WSNs), are reviewed. Various energy harvesting models which can increase the sustainability of the energy supply required for IoT devices are also discussed. Furthermore, the challenges which need to be overcome to make IoT-enabled sensors more durable, reliable, energy-efficient, and economical are identified.

Electricity demand is increasing, as a result of increasing consumers in the electricity market. By growing smart technologies such as smart grid and smart energy management systems, customers were given a chance to actively participate in demand response programs (DRPs), and reduce their electricity bills as a result. This study overviews the DRPs and their practices, along with home energy management systems (HEMS) and load management techniques. The paper provides brief literature on HEMS technologies and challenges. The paper is organized in a way to provide some technical information about DRPs and HEMS to help the reader understand different concepts about the smart grid, and be able to compare the essential concerns about the smart grid. The article includes a brief discussion about DRPs and their importance for the future of energy management systems. It is followed by brief literature about smart grids and HEMS, and a home energy management system strategy is also discussed in detail. The literature shows that storage devices have a huge impact on the efficiency and performance of energy management system strategies.

The rapid rise in the number of fossil fuel uses over the last few decades has increased carbon dioxide (CO2) emissions. The purpose of implementing renewable energy solutions, such as solar, hydro, wind, biomass, and other renewable energy sources, is to mitigate global climate change worldwide. Solar energy has received more attention over the last few decades as an alternative source of energy, and it can play an essential role in the future of the energy industry. This is especially true of energy solutions that reduce land use, such as off-grid and on-grid solar rooftop technologies. This study aims to evaluate the energy conversion efficiency of photovoltaic (PV) systems in tropical environments. It also explores the effect of growing plants beneath PV panels. Two identical grid-connected PV systems—each containing five solar panels—were installed. The overall power production of each PV system was about 1.4 kWp. All the collected data were processed and analysed in the same way and by the same method. The PV systems were installed in two different environments—one with the possibility of growing the plants beneath the PV panels (PViGR module) and one with no possibility of growing the plants beneath the PV panels (PViSR module). The experiments were conducted in the Bo Yang District of Songkhla, Thailand over a 12-month period. Our findings indicate that green roof photovoltaic (GRPV) systems can produce around 2100 kWh of electricity in comparison to the 2000 kWh produced by other solar energy systems. Thereby, growing plants beneath PV panels increases electricity production efficiency by around 2%. This difference comes from the growing of plants underneath GRPV systems. Plants do not only help to trap humidity underneath GRPV systems but also help to cool the PV panels by absorbing the temperature beneath GRPV systems. Thus, in the production of electrical energy; the system was clearly showing significant differences in the mentioned results of both PV solar systems, which are evident for great energy efficiency performances in the future.

Abstract Research interests on various scientific aspects of photovoltaic (PV) systems has increased over the past decade. However, these systems are still undergoing further developments, and new designs are being demonstrated every year. To minimize cost, reduce size, and increase the efficiency of PV systems, the use of transformerless PV grid-connected inverters has gained the interest of the residential market. This study describes the main challenges in transformerless topologies as well as provides a review on new single-phase grid-connected PV systems, which are categorized into six groups based on the number of switches required in the system. The basic operational principles of various schemes under the six categories of inverters are presented and compared in terms of leakage current, efficiency, strengths and weaknesses. Furthermore, the proposed inverter structure and a compensation strategy for eliminating leakage current have been discussed followed by a comparative study with the available ones. Consequently, our proposed system has found noteworthy results in PSIM environment with a reduction in leakage current as compared with those in three other different topologies.

This study assessed suitable smart grid areas for power generation and distribution from solar and small hydro energy resources in Western Uganda by employing the fuzzy analytic hierarchy process (AHP) based on geographic information system (GIS) data. This was performed based on the selected economic, environmental, and technical criteria by the authors guided by the experts’ judgements in the weighing process. The main criteria also included various sub-criteria. The sub-criteria of the economic criterion included distance from transmission lines, topography, and distance to roads. The environmental sub-criteria covered land use, sensitive areas, and protected areas. The technical sub-criteria were on distance from demand centers, available potential energy resources (solar and hydro), and climate (rainfall and sunshine). The weights of the main criteria and the sub-criteria were calculated by using the fuzzy AHP. These weights were then used in the GIS environment to determine both the potential for power generation from the solar energy resource and the smart grid suitable areas. According to the weight results, the economic criteria has the highest weight, followed by environmental and technical criteria. The validation of the experts’ judgements for each criterion by comparing the results from fuzzy AHP with AHP confirmed insignificant differences in weights for all criteria. The obtained suitable smart grid areas in Western Uganda have been classified into three parts, that is, the South, North, and Central. Therefore, this is a one-of-a-kind study that, in the authors’ view, will provide the initial insights to the government, policymakers, renewable energy practitioners, and researchers to investigate, map, and embrace decarbonization strategies for the electricity sector of Uganda.

With the growth in smart technology, customers have a chance to contribute to demand response programs and reduce their bills of electricity actively. This paper presents an intelligent wireless smart plug demonstration, which is designed to control the electrical appliances in the home energy management system (HEMS) application with a response to the utility company’s signal. Besides, a linear model of an energy management system utilizing a dynamic priority for electrical appliances is used as an energy management strategy. This can be useful for decreasing energy consumption in peak hours. Proposed hardware is tested with two different price strategies such as real-time pricing and a combination of this and incremental block rate (IBR) pricing. A small one-story house with ordinary electrical appliances is used as a test-bed for the proposed hardware and strategy. Initial results show that intelligent plugs can decrease the energy cost by 9% per day with an effective peak-to-average ratio deduction compared to the domicile without deploying intelligent plugs and controllers.

Amid growing demand for solar photovoltaic (PV) energy, the output from PV panels/cells fails to deliver maximum power to the load, due to the intermittency of ambient conditions. Therefore, utilizing maximum power point tracking (MPPT) becomes essential for PV systems. In this paper, a novel internet of things (IoT)-equipped MPPT solar charge controller (SCC) is designed and implemented. The proposed circuit system utilizes IoT-based sensors to send vital data to the cloud for remote monitoring and controlling purposes. The IoT platform helps the system to be monitored remotely. The PIC16F877A is used as a main controller of the proposed MPPT-SCC besides implementing the perturb and observe (P&O) technique and a customized buck–boost converter. To validate the proposed system, both simulation and hardware implementation are carried out by the MATLAB/SIMULINK environment and laboratory set up, respectively. The proposed MPPT-SCC can handle the maximum current of 10 A at 12 V voltage. Results show that the efficiency of the proposed system reaches up to 99.74% during a month of performance testing duration.