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.

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.