• Energy Research

Solar is a significant renewable energy source. Solar energy can provide for the world’s energy needs while minimizing global warming from traditional sources. Forecasting the output of renewable energy has a considerable impact on decisions about the operation and management of power systems. It is crucial to accurately forecast the output of renewable energy sources in order to assure grid dependability and sustainability and to reduce the risk and expense of energy markets and systems. Recent advancements in long short-term memory (LSTM) have attracted researchers to the model, and its promising potential is reflected in the method’s richness and the growing number of papers about it. To facilitate further research and development in this area, this paper investigates LSTM models for forecasting solar energy by using time-series data. The paper is divided into two parts: (1) independent LSTM models and (2) hybrid models that incorporate LSTM as another type of technique. The Root mean square error (RMSE) and other error metrics are used as the representative evaluation metrics for comparing the accuracy of the selected methods. According to empirical studies, the two types of models (independent LSTM and hybrid) have distinct advantages and disadvantages depending on the scenario. For instance, LSTM outperforms the other standalone models, but hybrid models generally outperform standalone models despite their longer data training time requirement. The most notable discovery is the better suitability of LSTM as a predictive model to forecast the amount of solar radiation and photovoltaic power compared with other conventional machine learning methods.

This study provides a techno-financial evaluation of two sites in Malaysia: Kudat, located on the coast of the northernmost part of Sabah, the state of East Malaysia with promising wind potential, and Putrajaya in the Klang Valley region with moderate wind potential at high elevations similar to the dominant cities in Malaysia. Three small-scale wind turbines were evaluated, taking into account a nominal electrical power generation below 100 kW. The research is focused on 220 residential households. The software used to perform the evaluation was Hybrid Optimization of Multiple Energy Resources (HOMER). The research novelty is the examination of the non-hybrid small-scale turbines at high elevations for regions with low wind speed, such as Malaysia. Regardless of the wind farms’ financial profit, this study used the net present cost (NPC) analysis in all cases. This research demonstrates the feasibility of small-scale wind turbines mounted at high elevations for generating sufficient energy. The results indicate that in both areas, the RX-20KH3 model is the best option, and the costs of the FH-5000 and RX-20KH3 farms are proportionate for a renewable project. Furthermore, with government support, the WES80 farm could be suitable.

Energy management and exchange have increasingly shifted from concentrated to hierarchical modes. Numerous issues have arisen in the decentralized energy sector, including the storage of customer data and the need to ensure data integrity, fairness, and accountability in the transaction phase. The problem is that in the field of the innovative technology of blockchain and its applications, with the energy sector still in the developmental stages, there is still a need for more research to understand the full capacity of the technology in the field. The main aim of this work was to investigate the state of the current research of blockchain technologies as well as their application within the field of energy. This work also set out to identify certain research gaps and provide a set of recommendations for future directions. Among these research gaps is the application of blockchain in decentralized storage, the integration of blockchain with artificial intelligence, and security and privacy concerns, which have not received much attention despite their importance. An analysis of fifty-seven carefully reviewed studies revealed that the emerging blockchain which provides privacy-protection technologies in cryptography and other areas that can be integrated to address users’ privacy concerns is another aspect that needs further investigation. Grid operations, economies, and customers will all learn from blockchain technology as it provides disintermediation, confidentiality, and tamper-proof transfers. Moreover, it provides innovative ways for customers and small solar generators to participate more actively in the electricity sector and to benefit from their properties. Blockchains are a rapidly evolving field of research and growth. A study of this emerging technology is necessary to increase comprehension, to educate the body of expertise on blockchains, and to realize its potential. This study recommends that future work investigates the potential application of blockchain in the energy sector as well as the challenges that face its implementation from the perspective of policy makers. This future approach will enable researchers to direct their focus to the case studies approach, which will facilitate and ease the application of blockchain technology.

The rapid development of smart technologies and data analytics empowers most industries to evolve their systems and introduce innovative applications. Consequently, smart metering technology, an internet of things-based application service, is diffusing rapidly in the energy sector. Regardless of its associated benefits, smart meters continue to struggle from consumers’ acceptance. To promote smart meters’ successful deployment, research is needed to better understand consumers’ acceptance of smart metering. Motivated by these concerns, a smart meter acceptance model is developed to evaluate the moderation role of experience and personal innovativeness factors among residential consumers. A cross-sectional research design was used in this study. Data were collected using a self-administrated questionnaire from 318 smart meters consumers who have had experience in using it. Hypothetical relationships were assessed and validated using partial least squares structural equation modelling. The empirical findings exert the moderating role of experience and personal innovativeness of smart meter acceptance that achieved an acceptable fit with the data, and specifically, five out of nine hypotheses were supported.

As applications generate massive amounts of data streams, the requirement for ways to analyze and cluster this data has become a critical field of research for knowledge discovery. Data stream clustering’s primary objective and goal are to acquire insights into incoming data. Recognizing all possible patterns in data streams that enter at variable rates and structures and evolve over time is critical for acquiring insights. Analyzing the data stream has been one of the vital research areas due to the inevitable evolving aspect of the data stream and its vast application domains. Existing algorithms for handling data stream clustering consider adding various data summarization structures starting from grid projection and ending with buffers of Core-Micro and Macro clusters. However, it is found that the static assumption of the data summarization impacts the quality of clustering. To fill this gap, an online clustering algorithm for handling evolving data streams using a tempo-spatial hyper cube called BOCEDS TSHC has been developed in this research. The role of the tempo-spatial hyper cube (TSHC) is to add more dimensions to the data summarization for more degree of freedom. TSHC when added to Buffer-based Online Clustering for Evolving Data Stream (BOCEDS) results in a superior evolving data stream clustering algorithm. Evaluation based on both the real world and synthetic datasets has proven the superiority of the developed BOCEDS TSHC clustering algorithm over the baseline algorithms with respect to most of the clustering metrics.

As wind energy is widely available, an increasing number of individuals, especially in off-grid rural areas, are adopting it as a dependable and sustainable energy source. The energy of the wind is harvested through a device known as a wind energy harvesting system (WEHS). These systems convert the kinetic energy of wind into electrical energy using wind turbines (WT) and electrical generators. However, the output power of a wind turbine is affected by various factors, such as wind speed, wind direction, and generator design. In order to optimize the performance of a WEHS, it is important to track the maximum power point (MPP) of the system. Various methods of tracking the MPP of the WEHS have been proposed by several research articles, which include traditional techniques such as direct power control (DPC) and indirect power control (IPC). These traditional methods in the standalone form are characterized by some drawbacks which render the method ineffective. The hybrid techniques comprising two different maximum power point tracking (MPPT) algorithms were further proposed to eliminate the shortages. Furtherly, Artificial Intelligence (AI)-based MPPT algorithms were proposed for the WEHS as either standalone or integrated with the traditional MPPT methods. Therefore, this research focused on the review of the AI-based MPPT and their performances as applied to WEHS. Traditional MPPT methods that are studied in the previous articles were discussed briefly. In addition, AI-based MPPT and different hybrid methods were also discussed in detail. Our study highlights the effectiveness of AI-based MPPT techniques in WEHS using an artificial neural network (ANN), fuzzy logic controller (FLC), and particle swarm optimization (PSO). These techniques were applied either as standalone methods or in various hybrid combinations, resulting in a significant increase in the system’s power extraction performance. Our findings suggest that utilizing AI-based MPPT techniques can improve the efficiency and overall performance of WEHS, providing a promising solution for enhancing renewable energy systems.

With the rise in the demand for electric vehicles, the need for a reliable charging infrastructure increases to accommodate the rapid public adoption of this type of transportation. Simultaneously, local electricity grids are being under pressure and require support from naturally abundant and inexpensive alternative energy sources such as wind and solar. This is why the world has recently witnessed the emergence of renewable energy-based charging stations that have received great acclaim. In this paper, we review studies related to this type of alternative energy charging infrastructure. We provide comprehensive research covering essential aspects in this field, including resources, potentiality, planning, control, and pricing. The study also includes studying and clarifying challenges facing this type of electric charging station and proposing suitable solutions for those challenges. The paper aims to provide the reader with an overview of charging electric vehicles through renewable energy and establishing the ground for further research in this vital field.