Prediction Of Energy Production Research Articles

Optimized Solar Energy Forecasting for Sustainable Development Using Machine Learning, Deep Learning, and Chaotic Models

This study applies four forecasting approaches—Ensemble Learning (EL), Deep Learning (DL), Machine Learning (ML), and Chaotic modeling— to predict energy production from the Konya Eregli solar power plant in Turkey. Using Python, it incorporates ambient temperature and solar cell temperature as exogenous variables alongside endogenous energy data. A year’s worth of 10-min interval data is trained, with two subsequent months forecasted by each model. The False Nearest Neighbors algorithm optimizes the embedding dimension for the chaotic analysis, and an optimized Echo State Network, achieving an R-squared above 0.97, is used for accurate forecasting. Additional models include Long-Short-Term Memory and Gated Recurrent Unit (DL), eXtreme Gradient Boosting and Random Forest (EL), and Extreme Learning Machine and Feed Forward Neural Network (ML). Each model is optimized using the Tree-structured Parzen Estimator, a Bayesian optimization approach. Evaluation metrics reveal all models performed well with the integration of endogenous and exogenous variables, with LSTM achieving the best results. This research advances solar energy forecasting, supporting Sustainable Development Goals (SDGs) related to affordable and clean energy, climate action, and sustainable communities through improved renewable energy management.

Wind turbine and PV power prediction using a deterministic data-driven model with variational mode decomposition preprocessing

This study develops a method for accurately forecasting solar radiation (SR), wind speed (WS), and air temperature (AT) for the coming 24 hours in order to predict energy production from photovoltaic (PV) panels and wind turbines (WT) in positive energy buildings. Input data are pre-processed through variational mode decomposition (VMD) for broadband feature extraction, which is then decomposed into smooth modes. The application of the Salp Swarm Algorithm (SSA) aims to optimize VMD parameters to enhance the precision of feature extraction. A thorough data analysis is performed to identify the essential input features. Residual pre-processing between input variables and their decomposed modes further enhances model performance. The stacking algorithm (SA) is used to predict both modes and residuals of the input data. Performance evaluation using metrics such as root mean square error (RMSE), normalized root mean square error (NRMSE), mean absolute error (MAE), and normalized mean absolute error (NMAE) indicates a reduction in error rates across measurement scales. For example, under adverse weather conditions, the NRMSE and NMAE for PV power are 2.50% and 1.95%, respectively.

How energy transition and fintech influence income inequality in Sub-Saharan Africa: Evidence from a panel-corrected standard error approach

This study explores the role of financial technology (FinTech) and the energy transition in shaping income inequality across 45 sub-Saharan African economies (SSA). By employing the panel-corrected standard errors approach, the analysis takes into account contextual and institutional factors, as well as regional specificities, for the period extending from 2000 to 2021. This research elucidates the connections among energy transition, FinTech, and income inequality, emphasizing their significance for economic progress and clean energy access. The findings reveal significant inequality in the outcomes of SSA countries, with an average Gini index of 43.197, indicating that most countries are unequal. This study reveals a notable connection between transitioning to renewable energy sources and a reduction in income inequality, with a one-unit increase in the energy transition indicator resulting in a 5.10% decrease in inequality. Moreover, FinTech is also found to contribute to a reduction in income inequality, with a 1.31% decrease observed for every unit increase in the FinTech indicator. The analysis also revealed a weak, yet statistically significant, link between population density and income inequality, with a one-unit increase in inequality of 0.00632%. Notably, industrial value added is found to be an influential factor with a statistically significant coefficient of 0.129. The key implications of the study emphasize the importance of redistributive policies, financial inclusion, and the promotion of renewable energy sources in constructing more inclusive economic systems. Furthermore, the study underscores the significant value of accurate energy production prediction for informed decision-making in energy planning and policy-making on regional and global scales.

Innovative Approaches in Residential Solar Electricity: Forecasting and Fault Detection Using Machine Learning

Recent advancements in residential solar electricity have revolutionized sustainable development. This paper introduces a methodology leveraging machine learning to forecast solar panels’ power output based on weather and air pollution parameters, along with an automated model for fault detection. Innovations in high-efficiency solar panels and advanced energy storage systems ensure reliable electricity supply. Smart inverters and grid-tied systems enhance energy management. Government incentives and decreasing installation costs have increased solar power accessibility. The proposed methodology, utilizing machine learning techniques, achieved an R-squared value of 0.95 and a Mean Squared Error of 0.02 in forecasting solar panel power output, demonstrating high accuracy in predicting energy production under varying environmental conditions. By improving operational efficiency and anticipating power output, this approach not only reduces carbon footprints but also promotes energy independence, contributing to the global transition towards sustainability.

Evaluation of techno-economic design andimplementation of solar-wind hybrid microgridsystem for a small community

Pakistan is faces significant challenges in meeting its energy demand and consumption needs for consumers. This country's energy production from primary sources such as petroleum and natural gasses is incompetent in fuel-use and hence unable to meet feasibility cost. With an increasing population, Pakistan's energy consumption per capita has been steadily rising. This behaviour is leading to critical energy issues, especially in remote rural areas. This trend in rising energy costs and demand factors are similar to those in the energy markets in the South and South-East Asia. The primary energy sources in Asia continent, including fossil fuels, are insufficient supply to meet this growing demand in production and thus resulting in frequent electricity blackouts. Consequently, renewable energy sources such as solar photovoltaic (PV) and wind power have substantially started to produce energy and to provide a huge portion of Pakistan's daily energy needs apart in conventional energy currently. However, these sources are not yet as reliable, conventional energy bases have a challenge for sustainable energy production. As a result, renewable energy factors nonetheless initial started have effectively stabilized energy consumption, particularly for green electricity with net-zero carbon emissions. The aim of this study is to evaluate the feasibility and cost-effectiveness of integrating a microgrid hybrid system with combined (solar PV/wind power) renewable energy as well as conventional fossil fuel generators. This evaluation focuses on predicting energy production and its costs using Hybrid Optimization of Multiple Energy Resources (HOMER) software, and to enhance the electricity standards at NUST (National University for Sciences and Technology), Pakistan. The proposed methodology of microgrid hybrid system, when evaluated using HOMER software, shows a significant improvement in energy stability and cost efficiency. Moreover, this proposed system can reduce reliance on fossil fuels by a substantial percentage, enhances the predictability of energy production, and optimizes its energy consumption. These can achieve better performance metrics in terms of reliability, cost, and environmental impact; feasible solution for Pakistan and the developing countries. This proposed methodology offers a novel approach by integrating renewable energy sources with conventional generators to create a balanced and efficiency factor by microgrid system. This hybrid system goals as an investigation is to optimize this energy production, reduce carbon emissions, and provide a more stable and cost-effective energy supply.

Predicting Energy Production in Renewable Energy Power Plants Using Deep Learning

It is very important to analyze and forecast energy production for investments in renewable energy resources. In this study, the energy production of wind and solar power plants, which are among the leading renewable energy sources, was estimated using deep learning. For a solar power plant, three different solar power plants with 1MW installed power were examined. Three-year energy production data of power plants were taken. These data were used with the deep learning method long short-term memory (LSTM) and seasonal autoregressive moving average (SARIMA). Results were obtained for each dataset; they were subjected to five different (MSE, RMSE, NMSE, MAE, and MAPE) error performance measurement systems. In the LSTM model, the highest accuracy rate was 81% and the lowest accuracy rate was 59%. In the SARIMA model, the highest accuracy rate was 66% and the lowest accuracy rate was 41%. As for wind energy, wind speeds in two different places were estimated. Wind speed data were taken from meteorological stations. Datasets were tested with MAPE, R2, and RMSE error performance measurement systems. LSTM, GRU, CNN-LSTM, CNN-RNN, LSTM-GRU, and CNN-GRU deep learning methods were used in this study. The CNN-GRU model achieved a maximum accuracy of 99.81% in wind energy forecasting.

Enhancing solar photovoltaic energy production prediction using diverse machine learning models tuned with the chimp optimization algorithm

Solar photovoltaic (PV) systems, integral for sustainable energy, face challenges in forecasting due to the unpredictable nature of environmental factors influencing energy output. This study explores five distinct machine learning (ML) models which are built and compared to predict energy production based on four independent weather variables: wind speed, relative humidity, ambient temperature, and solar irradiation. The evaluated models include multiple linear regression (MLR), decision tree regression (DTR), random forest regression (RFR), support vector regression (SVR), and multi-layer perceptron (MLP). These models were hyperparameter tuned using chimp optimization algorithm (ChOA) for a performance appraisal. The models are subsequently validated on the data from a 264 kWp PV system, installed at the Applied Science University (ASU) in Amman, Jordan. Of all 5 models, MLP shows best root mean square error (RMSE), with the corresponding value of 0.503, followed by mean absolute error (MAE) of 0.397 and a coefficient of determination (R2) value of 0.99 in predicting energy from the observed environmental parameters. Finally, the process highlights the fact that fine-tuning of ML models for improved prediction accuracy in energy production domain still involves the use of advanced optimization techniques like ChOA, compared with other widely used optimization algorithms from the literature.

Machine learning-based short-term solar power forecasting: a comparison between regression and classification approaches using extensive Australian dataset

Solar energy production is an intermittent process that is affected by weather and climate conditions. This can lead to unstable and fluctuating electricity generation, which can cause financial losses and damage to the power grid. To better control power production, it is important to predict solar energy production. Big data and machine learning algorithms have yielded excellent results in this regard. This study compares the performance of two different machine learning approaches to solar energy production prediction: regression and classification. The regression approach predicts the actual power output, while the classification approach predicts whether the power output will be above or below a certain threshold. The study found that the random forest regressor algorithm performed the best in terms of accuracy, with mean absolute errors and root mean square errors of 0.046 and 0.11, respectively. However, it did not predict peak power values effectively, which can lead to higher errors. The long short-term memory algorithm performed better in classifying peak power values. The study concluded that classification models may be better at generalizing than regression models. This proposed approach is valuable for interpreting model performance and improving prediction accuracy.

Application of the Metalog Probability Distribution Family to Predict Energy Production by Photovoltaic Systems for the Purposes of Generating Green Hydrogen

Application of the Metalog Probability Distribution Family to Predict Energy Production by Photovoltaic Systems for the Purposes of Generating Green Hydrogen

Demand side management through energy efficiency measures for the sustainable energy future of Pakistan

Pakistan is facing energy crises due to localized shortages, market manipulation, infrastructure disruption, rising demand, governance issues, climate and geopolitical events. In this situation Demand Side Management (DSM) is a promising solution to overcome the problem of energy crises. DSM strategy helps to manage consumer demand through energy conservation rather than to addition of new power capacity. In this study, Low Emissions Analysis Platform (LEAP) develops an energy model for Pakistan for the period 2022–2050. Three scenarios has been to constructed namely Baseline (BAS), Green Energy Policy (GEP), and Energy Efficiency (ENE) to predict the future energy demand, production, carbon emissions and the investment cost which covers capital, operational and maintenance costs. The model results suggest that DSM targets should be achieved through the implementation of ENE scenario. Predicted energy production and consumption under the ENE scenario are substantially less than those under the BAS scenario. The country can meet its 635.83,000 GWh energy demand with its 747.15,000 GWh energy production. Non-renewable sources produce 171.27,000 GWh, whilst renewable sources produce 575.88,000 GWh. According to this scenario, by 2050, CO2 emissions will be produced around 93.16 million metric tons, requiring an investment cost of $46.80 billion for building new power capacity. The study provides a roadmap with a suggestive optimal balance between energy saving with DSM approach and utilizing renewable energy production to meet energy demand for different sectors of the economy.

Zastosowanie metod uczenia maszynowego do predykcji generowanej energii w małym systemie PV

The article presents data analysis for predicting energy production in photovoltaic (PV) power plant systems. The purpose of long-term forecasts is to determine the effectiveness of preventive actions and manage the power system effectively. Climate variables affecting the production of electricity in photovoltaic systems were analyzed. Forecasting methods using machine learning techniques such as Multi-Layer Perceptron (MLP) neural networks and Support Vector Machine (SVM) were compared. In addition, metrics were selected to determine the quality of forecasts. Determining the quality of forecasts was based on the actual varying conditions, not on the weather forecast data. The way of data preparation to create forecasting models were presented and the models with the best metrics were selected. For this purpose, the Scikit-learn library was used to create scripts in Python. The best results were obtained for regression models: MLPRegressor, CatBoostRegressor and Support Vector Regression. Actual measurement data from a system of optimally-positioned panels with a power of 3.0 kWp were used. For the MLPRegressor model, the highest coefficient of determination 0.605 was achieved with the smallest root-mean-square error of 1.79 KWh.

Enhancing Kurdistan's manufacturing companies' sustainable waste management: A norm activation approach to green accounting, CSR, and environmental auditing oversight

The significance of accurate energy production prediction cannot be overstated, especially in the context of achieving carbon neutrality and balancing traditional and clean energy sources. Unlike conventional models with simplified assumptions or limited data inputs hindering energy usage optimization, waste reduction and efficient resource allocation, we introduced a novel structural equation modelling approach to eight manufacturing industries' sustainable waste management practices (SWMPs) in Iraq. This comprehensive analysis, conducted with Smart PLS software on 375 responses aims to enhance energy production predictions’ accuracy and support sustainability goals contribute to achieving carbon neutrality goals and promote a balanced energy mix that supports sustainability and environmental stewardship. The findings reveal noteworthy insights: notably, chemical manufacturing companies exhibit a substantial advantage from green accounting practices, witnessing a 78.1 % and 45.8 % improvement in environmental auditing oversight and SWMPs, respectively, compared to other manufacturing sectors. Compared to conventional grey models, our model demonstrates that a 1-unit improvement in CSR enhances environmental auditing oversight effectiveness by 33.4 % and sustainable waste management by 56.9 % across industries. By leveraging these data-driven insights and innovative approaches, we can drive positive change towards a more sustainable and resilient energy future, collectively contributing to a more resilient, efficient, and sustainable energy ecosystem that benefits societies, economies, and the environment. The heightened accuracy of energy production prediction facilitated by our novel model empowers stakeholders at regional and global levels to make informed decisions, mitigate risks, support policy development, achieve sustainability goals, formulate effective policies and foster collaboration.

A multi-fidelity approach for wind farm simulations and comparison with field data

The prediction of energy production and structural loads within wind farms is of high interest for wind industries to optimize the wind farm design and control under a variety of atmospheric conditions. However, it is still a key challenge to predict them accurately and efficiently due to the complex interactions between wind turbines and turbulent flows. Nowadays, high-fidelity simulations using Reynolds-Averaged Navier-Stokes (RANS) or Large-Eddy Simulation (LES) coupled with actuator disk or actuator line method are widely developed and used in wind farm analysis, but it still needs a huge computational resource, especially in the application of a commercial wind farm with large number of turbines. In this context, a mid-fidelity simulation tool based on the Dynamics Wake Meandering (DWM) model called FAST.Farm has been developed by National Renewable Energy Laboratory (NREL) in order to tackle this challenge. It allows to capture essential physics at the turbine scale as well as at the farm scale in a computational efficient manner. This study focuses on the calibration of DWM model which is the key to minimize inaccuracies between mid-fidelity and high-fidelity simulations regarding key performance indicators, e.g. thrust and power production. The calibration is made for DTU10MW wind turbine and shows a good improvement in accuracy compared to default parameters. The calibrated DWM model is finally employed to forecast the power generation for two turbines within a reference wind farm. A good improvement on the prediction is also obtained thanks to the calibrated parameters.

Modeling and mapping solar energy production with photovoltaic panels on Politecnico di Torino university campus

Educational institutions have significant impacts on the society and environment they are inhabiting, and they can have a big role in influencing various development fields, including sustainability. The environmental sustainability of universities was critically analyzed recently. These bodies can contribute to the sustainability of cities due to their social role in shaping the future generations. The aim of this work is to analyze Urban Building Energy Modeling with a place-based approach using the open-source software QGIS in predicting energy production with photovoltaic solar technologies on the rooftops of the central university campus of Politecnico di Torino. This modeling can help in assessing the energy security and affordability of current and future sustainable scenarios considering their impact on climate change. This study evaluates the accuracy of urban scale QGIS-based energy modeling with a comparison of measured data available from the monitoring activity of LivingLab of Politecnico di Torino, the free tool PVGIS, and the web tools of ENEA. The QGIS modeling accuracy depends on the different precisions of the Digital Surface Model used to describe the built environment (i.e., 1 m or 5 m) and the climate input data (monthly and annual diffuse-to-global radiation and Linke turbidity factor). Moreover, this assessment can be used to map the results of new photovoltaic systems improving the energy and environmental performance of university campuses. The results of this work shed light on the significance of different input data for energy simulation tools at neighborhood-urban scale. The result shown accuracies in PV production of 10 to 37% with different spatial resolutions of the 3D built environment and of 14 to 15.2% for temporal resolution of solar irradiation variables.

Environmental impacts of the economy, tourism, and energy consumption in Kuwait

In emerging economies like Kuwait, tourism funds national development and sustains local livelihoods. The rapid rise of tourism in several nations may have serious environmental effects due to increased energy usage that increases carbon dioxide (CO2) emissions and worsening global warming. Due to climate change's growing effects on the environment, several efforts have been made by numerous countries worldwide to achieve carbon neutrality and environmental sustainability. Moreover, effective energy production prediction is crucial to establish an equilibrium between traditional and clean energy sources. However, there is a research gap investigating the environmental impacts of tourism within the framework of energy-economy-environment nexus in Kuwait's context that propelled the development of this pioneering investigation. The present study's objective is to determine how economic growth, energy consumption, and tourism affect carbon emissions in Kuwait. This study analyzes Kuwait's time series data from 1995 to 2019 by using the autoregressive distributed lag (ARDL) method that analyzed the components' relationships, allowing for long-term and short-term dynamics. The results show that increasing economic growth, energy consumption, and tourist arrivals increase CO2 emissions in Kuwait. This article proposes policies to achieve carbon neutrality in Kuwait by expanding renewable energy use and promoting sustainable tourism to boost the economy.

Accelerating renewables: Unveiling the role of green energy markets

The innovative local grey forecasting model for energy production prediction emerges as a pivotal tool in the shift towards renewable energy, embodying the essence of adaptability and precision for the urgent need for sustainable solutions. The integration of such forecasting models is imperative for comprehending the complexities of energy production and market dynamics, ultimately facilitating a seamless transition to a sustainable energy future. Given this, the current study investigates the connections between renewable energy consumption and its determinants, focusing on the role of green energy markets across 12 countries from 2003 to 2021. Employing a novel quantile regression approach, this study analyzes the impact of green energy markets, GDP per capita, urban population, and environmental policies on renewable energy consumption, introducing a renewable energy consumption index for novel insights. Rooted in grey system theory, this study offers a unique approach to predicting energy outputs, crucial for guiding the strategic decisions in green energy markets. The quantile regression analysis reveals that green energy markets have a differential impact on renewable energy consumption whereas Renewable Energy Installed Capacity positively affects renewable energy consumption across all quantiles. Environmental Stringency Policies consistently show a negative impact across all quantiles. By providing reliable predictions of energy production, these findings empower policymakers, investors, and operators to make strategic, data-driven decisions that align with the fluctuating demands of the market and the overarching goals of sustainability and environmental stewardship.

A novel local grey model considering fluctuation based on kernel function for energy production prediction

The structure of energy production is constantly evolving, particularly in a low-carbon context, to align with the development of various industries. To pursue the global goal of carbon neutrality, it is essential to strike a balance between traditional energy and clean energy. Thus, it is of great practical significance to forecast different energy production effectively. Firstly, this paper proposes a novel local grey forecasting model to predict different energy production. Energy production has fluctuated in the past few years due to government policies. Secondly, the novel local grey forecasting model focuses on each year’s production in the modeling process and uses a kernel function to measure the validity of the information. Meanwhile, different from other grey models, the dynamic model proposed makes it possible that there are no static parameters for the overall prediction. Then, the window width in the local model is determined using the particle swarm optimization algorithm. Furthermore, energy production prediction including traditional energy and clean energy production in total North America, Europe and Asia Pacific is conducted. Results show that the novel local grey model has satisfactory prediction accuracy compared with other models, whose MAPEs have controlled under 5% for different energy production. Finally, reasonable suggestions for energy production are proposed to meet various energy needs.

Solution for Integration of Renewable Energy Power Plants into Smart Grids with Active Power Control

This article addresses the integration of renewable energy power plants into smart grids and active power control. Renewable energy sources contribute to environmentally friendly and sustainable energy production, but the fluctuations inherent in these sources pose a challenge for energy grids. The article examines various technologies that can be used to overcome this challenge and make energy grids more reliable. Smart grids aim to improve energy grids by optimizing energy production, transmission, and distribution using data analytics, automation, and communication technologies. The integration of renewable energy power plants into these smart grids offers significant advantages, including the ability to predict energy production, integrate with energy storage systems, and manage energy demand. The article also emphasizes the importance of active power control. Active power control is used to manage energy production steadily, thereby maintaining grid stability. Balancing energy fluctuations from renewable energy sources and storing excess energy when needed enhances grid stability. In conclusion, this article discusses the crucial role of integrating renewable energy power plants into smart grids and implementing active power control in the energy sector. These integration and control methods are important steps in making energy grids more sustainable, efficient, and reliable.

Time-dependent photovoltaic performance assessment on a global scale using artificial neural networks

The integration of Renewable Energy Sources (RESs), particularly solar PhotoVoltaics (PVs) has become an imperative aspect of sustainable energy systems. In this pursuit, accurate and efficient simulation tools play a pivotal role in optimizing the performance of PV systems. Traditional simulation approaches, while effective, are often characterized by computational complexities and time-intensive processes. This paper introduces a groundbreaking paradigm in solar energy modeling by harnessing the power of Artificial Neural Networks (ANNs) to revolutionize the accuracy and reliability of PV system simulations. In this work, an hourly, daily, monthly and yearly comparison of the electrical energy obtained with the 5-parameter model and those obtained with the ANNs was developed. For this purpose, a very wide ensemble of localities around the world and types of PV systems were considered in the training and validation phase. ANNs exhibited a maximum mean absolute relative error of 3.5% during training and consistently maintained hourly relative errors below 5% across diverse localities during validation. Hourly power forecasting remains acceptable also in localities with extreme weather conditions. Monthly errors peak at high negative and positive latitudes in summer months when daylight duration exceeds nighttime. However, in the least accurate locality, yearly energy forecasting yielded a maximum error of 8%. Empirical equations based on the trained ANNs are proposed and a relative input-output importance criterion was applied to detect the impact of air temperature and solar radiation on the performance of each PV module. The proposed ANNs demonstrate significant utility in decision-making and real-time processes, providing a valuable framework for managing energy flows within a network and predicting energy production during specific time intervals. This alternative approach surpasses conventional dynamic simulation methodologies found in existing literature in terms of computational cost with comparable accuracy.

Global horizontal irradiance prediction for renewable energy system in Najran and Riyadh

Producing and supplying energy efficiently are important for many countries. Using models to predict energy production can help reduce costs, improve efficiency, and make energy systems work better. This research predicts solar electricity production in the Najran and Riyadh regions of Saudi Arabia by analyzing 14 weather factors. The weather factors that were considered in the study include date, time, Global Horizontal Irradiance (GHI), clear sky, top of atmosphere, code, temperature, relative humidity, pressure, wind speed, wind direction, rainfall, snowfall, and snow depth. GHI is the most important factor because it determines how much solar energy a system can produce. Therefore, it is important to be able to predict GHI accurately. This study used a variety of data-driven models to predict GHI, including the elastic net regression, linear regression, random forest, k-nearest neighbor, gradient boosting regressor, light gradient boosting regressor, extreme gradient boosting regressor, and decision tree regressor. The models were evaluated using a set of metrics, including the mean absolute error, mean squared error, root mean square error, coefficient of determination (R2), and adjusted coefficient of determination. This study found that the decision tree regression, Random Forest (RF), and Extreme Gradient Boosting (XGB) models performed better in the Riyadh region than in the Najran region. The R2 values for the Riyadh region were 99%, 99%, and 98%, while the R2 values for the Najran region were 89%, 94%, and 94%. This suggests that the Riyadh region is a more suitable location for solar energy conversion systems. These findings are important for policymakers and investors who are considering the development of solar energy projects in Saudi Arabia.