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Space weather (SpW) is a phenomenon caused by a variety of solar events and has the potential to disrupt infrastructure systems and technology, putting them at risk. Despite SpW's immense impact, there has been a notable absence of bibliometric analysis studies to understand the research trends, regional distribution, social structure, conceptual structure, and knowledge gaps. This review synthesized scopus documents of SpW domain from 1988 to 2021. In this study, three tools were used, such as Microsoft Excel, VOSviewer, and Harzing's Publish or Perish for statistical analysis, graphical presentation, and citation metrics, respectively. Based on the 3,956 articles, roughly 70% of the articles were published in the last ten years, reveals a rapid growth in SpW research. The study discovered that China ranked third in publication volume, following the United States and the United Kingdom with Russian Federation following closely in fourth place. This study also presents six key findings, including the growth pattern of publications, contributions, and authorship collaboration by countries, most productive and influenced authors, co-authorship status, most influenced journals and articles, research cluster and new SpW subtopics discovered. These findings provide useful insight and aid in the advancement and progress of this field. [ABSTRACT FROM AUTHOR]

In recent years, the installed capacity increment with regard to solar power generation has been highlighted as a crucial role played by Photovoltaic (PV) generation forecasting in integrating a growing number of distributed PV sites into power systems. Nevertheless, because of the PV generation's unpredictable nature, deterministic point forecast methods struggle to accurately assess the uncertainties associated with PV generation. This paper presents a detailed structured review of the state-of-the-art concerning Probabilistic Solar Power Forecasting (PSPF), which covers forecasting methods, model comparison, forecasting horizon and quantification metrics. Our review methodology leverages the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to systematically identify primary data sources, focusing on keywords such as probabilistic forecasting, Deep Learning (DL), and Machine learning (ML). Through an extensive and rigorous search of renowned databases such as SCOPUS and Web of Science (WoS), we identified 36 relevant studies (n=36). Consequently, expert scholars decided to develop three themes: (1) Conventional PSPF, (2) PSPF utilizing ML, and (3) PSPF using DL. Probabilistic forecasting is an invaluable tool concerning power systems, especially regarding the rising proportion of renewable energy sources in the energy mix. We tackle the inherent uncertainty of renewable generation, maintain grid stability, and promote efficient energy management and planning. In the end, this research contributes to the development of a power system that is more resilient, reliable, and sustainable. [ABSTRACT FROM AUTHOR]

The present study proposes a geomagnetically induced currents (GICs) prediction model for three low-latitude locations, Huancayo, Peru, Addis Ababa, Ethiopia and Guam, United States. It employs the long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) neural networks. The model's performance was accessed using the interleaving odd-even data split (IntOE) approach as a benchmark. The geo-magnetic field variation (dB/dt) derived from geomagnetic disturbance event on 31 March 2001, is applied as the GICs' proxy. Results showed that employing both models, LSTM and BiLSTM with block division data split markedly enhanced prediction accuracy by up to 66% compared to IntOE. However, IntOE proves to be more effective for event-based validation. [ABSTRACT FROM AUTHOR]

Electric Energy Consumption (EEC) prediction for building operations can be performed using a Baseline Energy Model (BEM), which is vital to ensure the efficiency of the EEC estimates with its respective independent variables. However, developing the BEM to represent the relationship between independent variables can be a complex task due to the EEC variability in an educational building that differs during its operation period. The best-suited BEM must be continuously improvised to achieve good modeling with accurate and reliable predictions that capture the building operations’ current dynamics. This study aims to conduct a comparative performance assessment between deep learning, machine learning, and statistical models to develop the BEM and, therefore, predict the EEC of the building for 24, 48, 72, and 96 hours, while considering the operation of the lecture weeks and the associated number of students and staff. The hours and temperature are considered as independent variables to be tested with residual error evaluations, whilst the correlation coefficient, coefficient of determination, and training time are also taken into account. Three models with different categories involving Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and AutoRegressive Integrated Moving Average with Exogenous inputs (ARIMAX) were compared, concluding that SVR was the best and can be used as a universal model in the Machine Learning Baseline Energy Model (MLBEM) studies. Accurate EEC prediction will offer a huge advantage for building operators to properly monitor, plan, and manage the EEC, hence avoiding excessive utility bills. [ABSTRACT FROM AUTHOR]

There are various factors that can cause the rise of Neutral-To-Ground Voltage (NTGV) in an educational building. However, based on the pattern of the recorded NTGV, it is difficult to identify the possible factors that contributed to the event. This situation could lead to the wrong decisions to mitigate the corresponding problem. Thus, the main objective of this paper is to verify the high NTGV events based on a model of the educational building's three-phase fourwire system. In this study, the developed educational building electrical network model is integrated with the model for events in a MATLAB/SIMULINK environment. According to the findings, high NTGV events in the building were caused by lightning and ground fault activities. This shows that the model is accurate and suitable to be used for analysing the high NTGV events in an educational building if they are detected. [ABSTRACT FROM AUTHOR]

Leaks in pipelines caused by acid are destructive to both economic growth and capital which should be avoided at all costs. Damage to underground pipelines is caused by a hard-to-find leak, the unavailability of a real-time monitoring system and the lack of a pipeline history database. The aim of this work is to develop an early warning system to detect acid leaking in the pipeline using Internet of Things (IoT) technology. To detect changes in the pH of acid soil parameters near the pipeline, two mechanisms are required: first, to provide an early warning before the leak is detected and second, to report the occurrence of the leak. The notification system is equipped with three LED indicators, each showing the offline, online and signal detection status. The novelty of the work is a prototype that can detect the acid leak in the pipeline and record the pH values in a database for future research. Using continuous pH monitoring, real-time analysis and a database, this system can detect leaks before they become a major problem. Consequently, the manufacturing industry will benefit from this initiative as it is automated, efficient and cost-effective. [ABSTRACT FROM AUTHOR]