Optimal operation of stand-alone microgrid considering emission issues and demand response program using whale optimization Algorithm

Microgrids are new technologies for integrating renewable energies into power systems. Optimal operation of renewable energy sources in standalone micro-grids is an intensive task due to the continuous variation of their output powers and intermittant nature. This work addresses the optimum operation of an independent microgrid considering the demand response program (DRP). An energy management model with two different scenarios has been proposed to minimize the costs of operation and emissions. Interruptible/curtailable loads are considered in DRPs. Besides, due to the growing concern of the developing efficient optimization methods and algorithms in line with the increasing needs of microgrids, the focus of this study is on using the whale meta-heuristic algorithm for operation management of microgrids. The findings indicate that the whale optimization algorithm outperforms the other known algorithms such as imperialist competitive and genetic algorithms, as well as particle swarm optimization. Furthermore, the results show that the use of DRPS has a significant impact on the costs of operation and emissions

Data Analytics and Prediction Model for Malaysian Covid 19 Vaccination Progress

SARS CoV-2 varieties keep developing, triggering disease outbreaks and delaying or even halting the opening of society and economies. In countries with high vaccination rates, there have been significant decreases in serious illness, hospitalization, and mortality. Nevertheless, vaccine availability is unequal internationally, with coverage varying from 1% to over 70%, primarily dependent on the nation's income. This study focuses on conducting data analytics and prediction model on the impact and intensity of the Covid-19 global vaccination trend compared to Malaysia. The country's vaccination performance is compared and analyzed with G7 countries such as Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States. Moreover,  the vaccination rate of Malaysia and several SEA countries have also been compared in this study. This study discusses vital information such as the type of vaccines and vaccination rates. Meanwhile, the prediction model's goal is to predict the country's future vaccination trend

Energy audit data for a resort island in the South China Sea

The data consists of actual generation-side auditing including the distribution of loads, seasonal load profiles, and types of loads as well as an analysis of local development planning of a resort island in the South China Sea. The data has been used to propose an optimal combination of hybrid renewable energy systems that able to mitigate the diesel fuel dependency on the island. The resort island selected is Tioman, as it represents the typical energy requirements of many resort islands in the South China Sea. The data presented are related to the research article “Optimal Combination of Solar, Wind, Micro-Hydro and Diesel Systems based on Actual Seasonal Load Profiles for a Resort Island in the South China Sea” [1]. Keywords: Tioman, South China Sea, Load profile, Renewable energy, Resort Island, Energy audi

Data from renewable energy assessments for resort islands in the South China Sea

Renewable energy assessments for resort islands in the South China Sea were conducted that involves the collection and analysis of meteorological and topographic data. The meteorological data was used to assess the PV, wind and hydropower system potentials on the islands. Furthermore, the reconnaissance study for hydro-potentials were conducted through topographic maps in order to determine the potential sites suitable for development of run-of-river hydropower generation. The stream data was collected for 14 islands in the South China Sea with a total of 51 investigated sites. The data from this study are related to the research article “Optimal combination of solar, wind, micro-hydro and diesel systems based on actual seasonal load profiles for a resort island in the South China Sea” published in Energy (Khan et al., 2015) [1]. Keywords: South China Sea, Solar radiation,wind speed, rainfall, microhydropower, PV system, Wind energy generation syste

Energy Management Opportunities Through Energy Efficiency Retrofit for Hostel Building

An energy audit conducted on a building is also known as a building energy assessment. It can summarise the whole idea of a building’s energy use. Conducting an audit can help business owners determine how much energy a building uses and how much of this energy is useful. It also determines where it is losing energy and which problem areas and fixes should prioritized to make the building more efficient and comfortable. An energy audit should be the first step before making and energy-saving improvements. A targeted audit was conducted on the selected hostel building to identify the energy management opportunities and possible implementation of energy efficiency retrofit techniques. Regression and CUSUM analysis were performed on the obtained data to study the pattern of energy consumption. Recommendations were made on the energy management opportunities for energy conversation and saving with the estimation of carbon footprint

Performance Evaluation of Solar PV Inverter Controls for Overvoltage Mitigation in MV Distribution Networks

The incorporation of real and reactive power control of solar photovoltaic (PV) inverters has received significant interest as an onsite countermeasure to the voltage rise problem. This paper presents a comprehensive analysis of the involvement of active power curtailment and reactive power absorption techniques of solar PV inverters for voltage regulation in medium voltage (MV) distribution networks. A case study has been conducted for a generic MV distribution network in Malaysia, demonstrating the effectiveness of fixed power factor control, Volt–Var, and Volt–Watt controls in mitigating overvoltage issues that have arisen due to the extensive integration of solar PV systems. The results revealed that the incorporation of real and reactive power controls of solar PV inverters aids in successfully mitigating overvoltage issues and support network operating conditions. Furthermore, the comparative analysis demonstrated the importance of employing the most appropriate control technique for improved network performance

Current Status, Scenario, and Prospective of Renewable Energy in Algeria: A Review

Energy demand has been overgrowing in developing countries. Moreover, the fluctuation of fuel prices is a primary concern faced by many countries that highly rely on conventional power generation to meet the load demand. Hence, the need to use alternative resources, such as renewable energy, is crucial in order to mitigate fossil fuel dependency, while ensuring reductions in carbon dioxide emissions. Algeria—being the largest county in Africa—has experienced a rapid growth in energy demand over the past decade due to the significant increase in residential, commercial, and industry sectors. Currently, the hydrocarbon-rich nation is highly dependent on fossil fuels for electricity generation, with renewable energy only having a small contribution to the country’s energy mix. However, the country has massive potential for renewable energy generation, such as solar, wind, biomass, geothermal, and hydropower. Therefore, the government aims to diversify away from fossil fuels and promote renewable energy generation through policies and renewable energy-related programs. The country’s Renewable Energy and Energy Efficiency Development Plan focuses on large scale solar, wind generation as well as geothermal and biomass technologies. This paper provides an update on the current energy position and renewable energy status in Algeria. Moreover, this paper discusses renewable energy (RE) policies and programs that aim to increase the country’s renewable energy generation and its implementation status

Optimal Operation of Stand-Alone Microgrid Considering Emission Issues and Demand Response Program Using Whale Optimization Algorithm

Microgrids are new technologies for integrating renewable energies into power systems. Optimal operation of renewable energy sources in standalone micro-grids is an intensive task due to the continuous variation of their output powers and intermittent nature. This work addresses the optimum operation of an independent microgrid considering the demand response program (DRP). An energy management model with two different scenarios has been proposed to minimize the costs of operation and emissions. Interruptible/curtailable loads are considered in DRPs. Besides, due to the growing concern of the developing efficient optimization methods and algorithms in line with the increasing needs of microgrids, the focus of this study is on using the whale meta-heuristic algorithm for operation management of microgrids. The findings indicate that the whale optimization algorithm outperforms the other known algorithms such as imperialist competitive and genetic algorithms, as well as particle swarm optimization. Furthermore, the results show that the use of DRPS has a significant impact on the costs of operation and emissions

Empowering decision-making in cardiovascular care: Exploratory data analysis and predictive models for heart attack risk

Acute myocardial infarction, commonly referred to as a heart attack, stands as one of the most lethal medical conditions, highlighting the pressing necessity for the effective management of cardiovascular disease. This involves conducting comprehensive data analysis and extracting knowledge essential for diagnosis, regulation, and treatment. Anticipating the occurrence of heart attacks presents a formidable challenge for healthcare professionals, given the intricate nature of the condition that demands both experience and a profound understanding. In the contemporary landscape of medicine, the concealed data landscape conceals invaluable insights that can significantly shape critical decision-making processes. In this research endeavor, a dataset comprising patient records is harnessed to predict an individual’s vulnerability to heart attacks. Advanced data visualization techniques are employed to identify pivotal trends and outliers, facilitating the extraction of meaningful and actionable conclusions. This study involves the development of three classifier models for heart attack prediction: Logistic Regression, K Nearest Neighbor, and Support Vector model

Optimizing sales strategy in the Indian automobile industry: Predicting future car prices using machine learning and demographic data

Demographics play a vital role in defining the size, distribution, and structure of a population. In the context of the automobile industry, business owners can leverage demographic insights to gauge the demand for vehicles and strategically align their sales efforts. Accurate sales forecasting is essential for long-term business strategy, providing manufacturers with a competitive advantage in optimizing production planning methods. This project utilizes large-scale automobile sales data to forecast car price variations in the coming months, considering factors such as purchase patterns, car models, and other relevant data. By analyzing different attributes from a past-year dataset, three machine learning algorithms: Linear Regression, Decision Tree Regression, and Random Forest Regression were employed to predict future car prices. The performance of each algorithm is evaluated using the R-squared value. Notably, the Random Forest regression model achieves a higher accuracy of 93%, outperforming both Decision Tree regression and Linear regression. These results demonstrate the suitability of Random Forest regression in predicting big data for the industry’s future product production plan and overall strategy