Harvesting Renewable Energy from Foot Pedal Using Rotational Electromagnetic Induction Flywheel

Philippines is one of the highest electricity prices in the ASEAN where harnessing renewable energy using wasted human effort is necessary. The global pandemic COVID-19 is spreading and because of this, establishments have required sanitation. The study's main objective is to Develop a Rotational Electromagnetic Induction Flywheel using Foot Pedal as Actuation to Harvest Renewable Energy. T-test was used to validate the results using the battery percentage of a power bank as the parameter, where there is a significant difference between single and multiple actuations with an attached mechanical dispenser and without. The system was able to harness an average of 0.30992 Watt-hour and 6.11476 Watt-hour in 5 daily trials for single and multiple controlled set-ups without mechanical dispenser respectively. An average of 0.2441 Watt-hour and 5.0027 Watt-hour for single and multiple controlled set-ups with mechanical dispenser correspondingly. Lastly, an average of 3.2924 Watt-hour in 5 daily trials for uncontrolled set-up. © 2023 IEEE.

A Review on Solar Energy Policy and Current Status: Top 5 Countries and Kazakhstan

The article describes the world's experience in developing the solar industry. It discusses the mechanisms of state support for developing renewable energy sources in the cases of five countries that are the most successful in this area—China, the United States, Japan, India, and Germany. Furthermore, it contains a brief review of state policy in producing electricity by renewable energy facilities in Kazakhstan. This paper uses statistical information from the International Renewable Energy Agency (IRENA), the International Energy Agency (IEA), British Petroleum (BP), and the Renewable Energy Network (REN21), and peer-reviewed sources. The research methodology includes analytical research and evaluation methods to examine the current state of solar energy policy, its motivators and incentives, as well as the prospects for its development in Kazakhstan and in the world. Research shows that solar energy has a huge development potential worldwide and is sure to take its place in gross electricity production. This paper focuses on the selected economic policies of the top five countries and Kazakhstan, in what may be considered a specific research limitation. Future research suggestions for the expansion of Renewable Energy (RE) in Kazakhstan could include analysing the impact of introducing dedicated policies and incentives for solar systems and exploring the benefits and challenges of implementing large RE zones with government–business collaboration.

Is irrigation fit for purpose? A review of the relationships between scheme size and performance of irrigation systems

Irrigation is increasingly being called upon to help stabilize and grow food and water security in the face of multiple crises;these crises include climate change, but also recent global food and energy price crises, including the 2007/08 food and energy price crises, and the more recent crises triggered by the COVID-19 pandemic and the war on Ukraine. While irrigation development used to focus on public, large-scale, surface- and reservoir-fed systems, over the last several decades, private small-scale investments in groundwater irrigation have grown in importance and are expected to see rapid future growth, particularly in connection with solar-powered pumping systems. But is irrigation 'fit-for-purpose' to support population growth, economic development, and multiple food, energy and climate crises? This paper reviews how fit-for-purpose irrigation is with a focus on economies of scale of surface and groundwater systems, and a particular examination of systems in Sub-Saharan Africa where the need for expansion is largest. The review finds challenges for both larger surface and smaller groundwater systems in the face of growing demand for irrigated agriculture and dwindling and less reliable water supplies. To support resilience of the sector, we propose both a holistic design and management improvement agenda for larger surface systems, and a series of suggestions to improve sustainability concerns of groundwater systems.

COVID19, the war in Ukraine, the economic crisis, the energy crisis, climate change

Each month brings new fears and new reasons to worry about the future. In a world marked by permanent change, by the occurrence of the unthinkable generating a continuous feeling of insecurity, having confidence has become increasingly difficult: confidence in the future, confidence in our environments, in our organisations, confidence in our contemporaries, confidence in our collaborators and confidence in our ability to face a difficult tomorrow. Confidence and fear are inseparable and they are like the opposite sides of the same coin. Unable to look at both sides of a coin at the same time, we must constantly fight our fears with confidence. Therefore, more than ever, trust is an essential element to obtain team security and it only takes one person feeling insecure for the overall confidence of the team to be eroded. This feeling of low self-confidence is particularly true for the younger generation of veterinary surgeons and specialized veterinary assistants. This can result in difficulties that are often unexpressed and can lead professionals to abandon these vocations of "passion" since they do not feel up to the expectations of clients and managers alike. Building the self-confidence of the people concerned has become a professional priority. The origin of the feeling of lack of self-confidence is collective. Therefore, its treatment is collective. Since each member has the capacity to fight against their fears, he or she can play their part and increase security in the team.

Changes in the Pattern of Weekdays Electricity Real Consumption during the COVID-19 Crisis

In this paper, using data from Romania, we analysed the changes in electricity consumption generated during the COVID-19 crisis, and the measures taken against the spread of the coronavirus to limit the effects of the pandemic. Using a seasonal autoregressive econometric model, we found that, beyond seasonal (weekly, monthly, quarterly, yearly) effects, the average daily electricity real consumption in Romania, during the state of the emergency period (16 March 16 to 14 May 2020) decreased by -194.8 MW (about -2.9%), compared to the historical data (2006-March 2022), and this decrease is not due to the action of some random factors, and it is not a manifestation of domain-specific seasonality. The literature discusses the hypothesis that during the pandemic time, the profile of daily electricity consumption on weekdays was close to the typical Sunday profile. We tested a similar hypothesis for Romania. As a methodology, we tried to go beyond the simple interpretation of statistics and graphics (as found in most papers) and we calculated some measures of distances (the Mahalanobis distance, Manhattan distance) and similarity (coefficient of correlation, cosines coefficient) between the vectors of daily electricity real consumptions, by hourly intervals. As the time interval, we have analysed, for Romania, the electricity real consumption over the period January 2006-March 2022, by day of the week and within the day, by hourly intervals (5911 observations). We found (not very strong) evidence supporting a hypothesis that, in the pandemic crisis, the profile of electricity consumption approaches the weekend pattern only for the state of the emergency period, and we could not find the same evidence for the state of the alert period (June 2020-March 2022). The strongest closeness is to the hourly consumption pattern of Saturday. That is, for Romania, in terms of electricity consumption, "under lockdown, every day is a Sunday" (Staffell) it is rather "under lockdown, every day is (almost) a Saturday"! During the state of the alert period, consumption returned to the pre-crisis profile. Since certain behaviours generated by the pandemic have been maintained in the medium and long term (distance learning, working from home, online sales, etc.), such studies can have policy implications, especially for setting energy policy measures (e.g., in balancing load peaks).

Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data

Due to extreme weather conditions and anomalous events such as the COVID-19 pandemic, utilities and grid operators worldwide face unprecedented challenges. These unanticipated changes in trends introduce new uncertainties in conventional short-term electricity demand forecasting (EDF) since its result depends on recent usage as an input variable. In order to quantify the uncertainty of EDF effectively, this paper proposes a comprehensive probabilistic EFD method based on Gaussian process regression (GPR) and kernel density estimation (KDE). GPR is a non-parametric method based on Bayesian theory, which can handle the uncertainties in EDF using limited data. Mobility data is incorporated to manage uncertainty and pattern changes and increase forecasting model scalability. This study first performs a correlation study for feature selection that comprises weather, renewable and non-renewable energy, and mobility data. Then, different kernel functions of GPR are compared, and the optimal function is recommended for real applications. Finally, real data are used to validate the effectiveness of the proposed model and are elaborated with three scenarios. Comparison results with other conventional adopted methods show that the proposed method can achieve high forecasting accuracy with a minimum quantity of data while addressing forecasting uncertainty, thus improving decision-making.

BIST Tourism and BIST Electricity Index Relationship

In this study, which is based on daily data, the relationship between BIST electricity index and BIST tourism index was measured between 2012:M9 – 2022:M9 periods. The aim of the study is to measure the relationship between BIST electricity index and BIST tourism index. VAR Granger causality test was applied to determine whether there is any causal relationship between the variables. It has been determined as a result of the analysis that the BIST electricity index has no effect on the BIST tourism index. Two-way ineffectiveness was determined among the variables. In addition, it was obtained as a result of the analysis that the applied correlation relationship was weak between these variables. The results obtained from the study are important in terms of measuring the effects among BIST indices.

From least cost to least risk: Producing climate change mitigation plans that are resilient to multiple risks

Our plans to tackle climate change could be thrown off-track by shocks such as the coronavirus pandemic, the energy supply crisis driven by the Russian invasion of Ukraine, financial crises and other such disruptions. We should therefore identify plans which are as resilient as possible to future risks, by systematically understanding the range of risks to which mitigation plans are vulnerable and how best to reduce such vulnerabilities. Here, we use electricity system decarbonization as a focus area, to highlight the different types of technological solutions, the different risks that may be associated with them, and the approaches, situated in a decision-making under deep uncertainty (DMDU) paradigm, that would allow the identification and enhanced resilience of mitigation pathways. Copyright © 2023 Gambhir and Lempert.

Analysis of the Czech Intraday Electricity Market During COVID-19 Pandemic from the Multifractal Perspective

This paper investigates a progress of the maturity of the Czech intraday electricity market during the COVID-19 pandemic by employing the multifractal analysis. Our results indicate that since intraday electricity returns display multifractal property originating both from long-range correlations and fat-tailed distribution, a sole use of the Hurst exponent is not sufficient, and multifractality characteristics should be used. The quantities describing a multifractal behavior indicate in some periods higher stage of market development operating on short temporal scales compared to the larger temporal scales, especially the MLM index. In some periods, they are in close agreement with the Hurst approach (e.g., July 2020). Moreover, the ADL models indicate a positive association of the Hurst exponent on short temporal scales with its lagged values and new cases of the COVID-19. On short temporal scales, the rate of new COVID-19 cases was positively related to the strength of multifractality, i.e., smaller degree of maturity, both by singularity spectrum width and MLM index. We found a nonlinear relationship between the government stringent policy and the Hurst exponent on long temporal scales, singularity spectrum width and the MLM index on short temporal scales, indicating that the loose anti-COVID policies are associated with more mature market and vice versa. On the contrary, on its long counterpart, the relationships are weaker and opposite in signs.

Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda.

The COVID19 pandemic has impacted the global economy, social activities, and Electricity Consumption (EC), affecting the performance of historical data-based Electricity Load Forecasting (ELF) algorithms. This study thoroughly analyses the pandemic's impact on these models and develop a hybrid model with better prediction accuracy using COVID19 data. Existing datasets are reviewed, and their limited generalization potential for the COVID19 period is highlighted. A dataset of 96 residential customers, comprising 36 and six months before and after the pandemic, is collected, posing significant challenges for current models. The proposed model employs convolutional layers for feature extraction, gated recurrent nets for temporal feature learning, and a self-attention module for feature selection, leading to better generalization for predicting EC patterns. Our proposed model outperforms existing models, as demonstrated by a detailed ablation study using our dataset. For instance, it achieves an average reduction of 0.56% & 3.46% in MSE, 1.5% & 5.07% in RMSE, and 11.81% & 13.19% in MAPE over the pre- and post-pandemic data, respectively. However, further research is required to address the varied nature of the data. These findings have significant implications for improving ELF algorithms during pandemics and other significant events that disrupt historical data patterns.

Projecting the Electricity Demand of the Residential Sector in Thailand During and After the COVID-19 Pandemic

The residential sector in Thailand has been a fast-growing energy consumption sector since 1995 at a rate of 6% per year. This sector makes a significant contribution to Thailand's rising electricity demand especially during the COVID-19 pandemic. This study projects Thailand's residential electricity consumption characteristics and the factors affecting the growth of electricity consumption using a system dynamics (SD) modeling approach to forecast long-term electricity consumption in Thailand. Furthermore, the COVID-19 pandemic and the lockdown can be seen as a forced social experiment, with the findings demonstrating how to use resources under particular circumstances. Four key factors affecting the electricity demand used in the SD model development include (1) work and study from home, (2) socio-demographic, (3) temperature changing, and (4) rise of GDP. Secondary and primary data, through questionnaire survey method, were used as data input for the model. The simulation results reveal that changing behavior on higher-wattage appliances has huge impacts on overall electricity consumption. The pressure to work and study at home contributes to rises of electricity consumption in the residential sector during and after COVID-19 pandemic. The government and related agencies may use the study results to plan for the electricity supply in the long term. © 2023 World Scientific Publishing Co.

Online Fuzzy Logic Prediction of Electrical Load Based on Real-Time Measurements During the Covid-19 Pandemic

As technologies advance and the population grows, electrical energy became one of the necessities for many peoples. Because the availability of electrical energy is limited, it requires various ways to be used efficiently. Electrical load monitoring usage in Indonesia still require an electrical officer to come to an electric panel location to record electrical usage. During the COVID-19 pandemic, it is not feasible to locally visit an electric panel because of the many restrictions. Remote monitoring using Internet of Things (IoT) can be used to address the problem. Going further, by knowing the electrical load usage, prediction can be done using fuzzy logic as a way to understand how to use electricity efficiently. Thus, a fuzzy logic load forecasting system IoT is developed in this research. Fuzzy variables used in this system are time of day, days of the week, measured loads, and forecasted loads. The research produced a system that predicts electrical load with one hour of accuracy based on the previous week's data. The average prediction error rate of the system is 9.48%. The implemented system is available on a web server and can be accessed via a web browser, either via a computer or cellphone. The system allows users to monitor and predict electrical load usage regardless of time and place. © 2023, The authors.

Application of Artificial Intelligence to Predict CO2 Emissions: Critical Step towards Sustainable Environment

Prediction of carbon dioxide (CO2) emissions is a critical step towards a sustainable environment. In any country, increasing the amount of CO2 emissions is an indicator of the increase in environmental pollution. In this regard, the current study applied three powerful and effective artificial intelligence tools, namely, a feed-forward neural network (FFNN), an adaptive network-based fuzzy inference system (ANFIS) and long short-term memory (LSTM), to forecast the yearly amount of CO2 emissions in Saudi Arabia up to the year 2030. The data were collected from the "Our World in Data” website, which offers the measurements of the CO2 emissions from the years 1936 to 2020 for every country on the globe. However, this study is only concerned with the data related to Saudi Arabia. Due to some missing data, this study considered only the measurements in the years from 1954 to 2020. The 67 data samples were divided into 2 subsets for training and testing with the optimal ratio of 70:30, respectively. The effect of different input combinations on prediction accuracy was also studied. The inputs were combined to form six different groups to predict the next value of the CO2 emissions from the past values. The group of inputs that contained the past value in addition to the year as a temporal index was found to be the best one. For all the models, the performance accuracies were assessed using the root mean squared errors (RMSEs) and the coefficient of determination (R2). Every model was trained until the smallest RMSE of the testing data was reached throughout the entire training run. For the FFNN, ANFIS and LSTM, the averages of the RMSEs were 19.78, 20.89505 and 15.42295, respectively, while the averages of the R2 were found to be 0.990985, 0.98875 and 0.9945, respectively. Every model was applied individually to forecast the next value of the CO2 emission. To benefit from the powers of the three artificial intelligence (AI) tools, the final forecasted value was considered the average (ensemble) value of the three models' outputs. To assess the forecasting accuracy, the ensemble was validated with a new measurement for the year 2021, and the calculated percentage error was found to be 6.8675% with an accuracy of 93.1325%, which implies that the model is highly accurate. Moreover, the resulting forecasting curve of the ensembled models showed that the rate of CO2 emissions in Saudi Arabia is expected to decrease from 9.4976 million tonnes per year based on the period 1954–2020 to 6.1707 million tonnes per year in the period 2020–2030. Therefore, the finding of this work could possibly help the policymakers in Saudi Arabia to take the correct and wise decisions regarding this issue not only for the near future but also for the far future.

Consumer Preferences for Smart Energy Services Based on AMI Data in the Power Sector

Advanced metering infrastructure (AMI) is becoming increasingly popular as an efficient means of energy demand management. By collecting energy data through AMI, it is possible to provide users with information that can induce them to change their behavior. To ensure that AMI continues to expand and to encourage the use of energy data, it is important to increase consumer participation and analyze their preferred service attributes. This study utilized a choice experiment to analyze consumer preferences for and acceptance of smart energy services based on AMI data. The results of a mixed logit model estimation show that consumers prefer the electricity information service for individual households and the social safety-net service among convergence services. A scenario analysis confirms that monetary compensation to offset any additional charges is important to maintain the level of consumer acceptance. These empirical findings offer insights for policymakers and companies seeking to develop policies and similar services.

Stock Price Forecasting of IBEX35 Companies in the Petroleum, Electricity, and Gas Industries

In recent years, time series forecasting has become an essential tool for stock market analysts to make informed decisions regarding stock prices. The present research makes use of various exponential smoothing forecasting methods. These include exponential smoothing with multiplicative errors and additive trend (MAN), exponential smoothing with multiplicative errors (MNN), and simple exponential smoothing with additive errors (ANN) for the forecasting of the stock prices of six different companies in the petroleum, electricity, and gas industries that are listed in the IBEX35 index. The database employed for this research contained the IBEX35 index values and stock closing prices from 3 January 2000 to 30 December 2022. The models trained with this data were employed in order to forecast the index value and the closing prices of the stocks under study from 2 January 2023 to 24 March 2023. The results obtained confirmed that although none of the proposed models outperformed the rest for all the companies, it is possible to calculate forecasting models able to predict a 95% confidence interval about real stock closing values and where the index will be in the following three months.

The Differential Effects of Oil Prices on the Development of Renewable Energy in Oil-Importing and Oil-Exporting Countries in Africa

The shift to renewable sources of energy has become a critical economic priority in African countries due to energy challenges. However, investors in the development of renewable energy face problems with decision making due to the existence of multiple criteria, such as oil prices and the associated macroeconomic performance. This study aims to analyze the differential effects of international oil prices and other macroeconomic factors on the development of renewable energy in both oil-importing and oil-exporting countries in Africa. The study uses a panel vector error correction model (P-VECM) to analyze data from five net oil exporters (Algeria, Angola, Egypt, Libya and Nigeria) and five net oil importers (Kenya, Ethiopia, Congo, Mozambique and South Africa). The study finds that higher oil prices positively affect the development of renewable energy in oil-importing countries by making renewable energy more economically competitive. Economic growth is also identified as a major driver of the development of renewable energy. While high-interest rates negatively affect the development of renewable energy in oil-importing countries, it has positive effects in oil-exporting countries. Exchange rates play a crucial role in the development of renewable energy in both types of countries with a negative effect in oil-exporting countries and a positive effect in oil-importing countries. The findings of this study suggest that policymakers should take a holistic approach to the development of renewable energy that considers the complex interplay of factors, such as oil prices, economic growth, interest rates, and exchange rates.

Asymmetrically examining the impact of green finance and renewable energy consumption on environmental degradation and renewable energy investment: The impact of the COVID-19 outbreak on the Chinese economy

The recent global recession due to Covid-19 has led to a drop in natural resource prices, which has contracted energy demand. Amid this concern, environmentally sustainable renewable energy projects have become uncompetitive and an obstacle to achieving the Sustainable Development Goals (SDGs). Following the nonlinear autoregressive distributed lag (NARDL) model proposed by Shin et al. (2014), to asymmetrically explore the impact of green bonds on renewable energy investment and environmental pollution and the impact of renewable energy consumption on environmental degradation in China over the period 1970–2020.​ The results show that the expansion of green bonds (GB+) significantly promotes renewable energy investment and reduces environmental pollution, while the contraction of green bonds (GB−) significantly reduces renewable energy investment and stimulates environmental damage. Likewise, expansion of renewable energy consumption (REC+) significantly reduced environmental degradation, while contraction of renewable energy consumption (REC−) significantly contributed to environmental degradation. Moreover, the result also validates the existence of inverted U-shaped EKC hypothesis in China The VECM Granger causality test indicate that renewable energy investment, green finance, renewable energy consumption, CO2 emission, and Gross Domestic Product (GDP) have long term causality. Chinese policymakers must focus on strengthening green finance, which will encourage renewable energy investment and renewable energy generation. Moreover, renewable electricity output greatly facilitates renewable energy investment, so China must innovate policies to take into account renewable electricity rather than fossil fuel generation in order to achieve the Sustainable Development Goals (SDGs).

Uncertainty management in electricity demand forecasting with machine learning and ensemble learning: Case studies of COVID-19 in the US metropolitans

Improving load forecasting is becoming increasingly crucial for power system management and operational research. Disruptive influences can seriously impact both the supply and demand sides of power. This work examines the impact of the coronavirus on power usage in two US states from January 2020 to December 2020. A wide range of machine learning (ML) algorithms and ensemble learning are employed to conduct the analysis. The findings showed a surprising increase in monthly power use changes in Florida and Texas during the COVID-19 pandemic, in contrast to New York, where usage decreased over the same period. In Texas, the quantity of power usage rises from 2% to 6% practically every month, except for September, when it decreased by around 1%. For Florida, except for May, which showed a fall of roughly 2.5%, the growth varied from 2.5% to 7.5%. This indicates the need for more extensive research into such systems and the applicability of adopting groups of algorithms in learning the trends of electric power demand during uncertain events. Such learning will be helpful in forecasting future power demand changes due to especially public health-related scenarios. © 2023 Elsevier Ltd

Tracking economic fluctuations with electricity consumption in Bangladesh

This paper establishes electricity consumption as an indicator for tracking economic fluctuations in Bangladesh. It presents monthly data on national electricity consumption since 1993 and subnational daily consumption data since February 2010. Electricity consumption is strongly correlated with other high-frequency indicators of economic activity, and it has declined during natural disasters and the COVID-19 lockdowns. The paper estimates an electricity consumption model that explains over 90% of the variation in daily consumption based on a quadratic trend, seasonality, within-week variation, national holidays, Ramadan, and temperature. Deviations from the model prediction can act as an indicator of subnational economic fluctuations. For example, electricity consumption in Dhaka fell around 40% below normal in April and May 2020 during the first COVID-19 lockdown and remained below normal afterwards. The later lockdowns, in contrast, had much smaller impacts, in line with less stringent containment measures and more effective adaptation.