Energy Production Forecast Research Articles

Solar energy prediction in IoT system based optimized complex-valued spatio-temporal graph convolutional neural network

The accurate prediction of solar energy generation is significant for efficient energy management in Internet of Things (IoT) devices. However, current forecasting models frequently fail to account for the dynamic nature of weather conditions. Also, traditional methods often have limited accuracy and scalability. This paper proposes Solar Energy Prediction in IoT system based optimized Complex-Valued Spatio-Temporal Graph Convolutional Neural Network (SEP-CVSGCNN-IoT) to overcome the limitations of existing models. Initially, the data are collected from solar panel and weather forecast. The collected data is given to the pre-processing using Data-Adaptive Gaussian Average Filtering (DAGAF) to remove the unwanted data and replace missing data. The pre-processed data is given into Nutcracker Optimization (NCO) algorithm for selecting optimal features. Then, the selected features are given to the Complex-Valued Spatio-Temporal Graph Convolutional Neural Network (CVSGCNN) for solar energy prediction. Finally, Dipper Throated Optimization Algorithm (DTOA) is proposed to enhance the weight parameter of CVSGCNN classifier, which precisely predicts solar energy in IoT. The proposed SEP-CVSGCNN-IoT method provides 18.46%, 26.34, 15.69 and 20.84% higher accuracy and 18.24%, 23.77, 24.34 and 16.29% lower mean absolute error when analyzed with existing techniques, such as deep learning enhanced solar energy prediction and AI-driven IoT (DL-ESEF-AI), towards efficient renewable energy prediction using deep learning (TEE-REP-DL), a new deep learning method for effectual forecasting of short-term PV energy production (DL-EF-SPEP) and metaheuristic-dependent hyper parameter tuning for recurrent deep learning: application to the solar energy generation prediction (HT-RDL-PSEG) respectively.

Long-term power forecasting of photovoltaic plants using artificial neural networks

Increasing the robustness of forecasting energy production models from a photovoltaic plant is crucial in investment decisions, while profitability is closely linked to its location since its energy production depends on the local climate conditions. This study aimed to mitigate uncertainty regarding the installation of photovoltaic systems by addressing the challenge of forecasting electrical energy production over the long term. Artificial Neural Network models were used for this purpose, predicting the power output of a photovoltaic plant based on the ambient temperature, cell temperature, and solar irradiance. Data recorded every minute over one year at an experimental photovoltaic plant revealed a strong correlation between energy production and the input variables. This research compared the performance of multilayer perceptron, feedforward, long short-term memory, and modular artificial neural networks architectures. Validation was carried out using mean squared error, mean absolute error, mean absolute percentage error, root mean squared error, and coefficient of determination as key metrics. Notably, the long short-term memory network demonstrated the highest accuracy in energy forecasting achieving a mean squared error of 0.0089 p.u, a mean absolute error of 0.0527 p.u, a root mean squared error of 0.0944 p.u, and a mean absolute percentage error of 49.0124 %. Nonetheless, the modular model came close to long short-term memory accuracy but with lower computational cost.

The impact of AI on boosting renewable energy utilization and visual power plant efficiency in contemporary construction

The integration of Artificial Intelligence (AI) in the renewable energy sector has revolutionized the efficiency and utilization of renewable energy sources in contemporary construction, significantly impacting visual power plant operations. AI technologies, including machine learning and predictive analytics, optimize energy production, enhance system performance, and streamline maintenance processes, thereby driving the adoption and efficiency of renewable energy systems. AI-powered predictive maintenance tools analyze vast amounts of data from renewable energy installations, such as solar panels and wind turbines, to predict and prevent equipment failures. This proactive approach reduces downtime and maintenance costs, ensuring continuous and efficient energy generation. Additionally, AI algorithms optimize energy storage and distribution by accurately forecasting energy production and demand. This optimization helps balance supply and demand, reducing reliance on non-renewable energy sources and enhancing grid stability. In visual power plants, AI enhances operational efficiency through advanced monitoring and control systems. Real-time data from sensors and IoT devices are processed by AI to provide actionable insights, enabling operators to make informed decisions promptly. AI-driven automation in visual power plants ensures optimal performance by adjusting parameters in real-time, based on environmental conditions and energy demand, thus maximizing energy output. Moreover, AI facilitates the design and construction of smart buildings and infrastructure, integrating renewable energy systems seamlessly. AI-driven energy management systems in buildings analyze consumption patterns, optimize energy use, and promote energy-saving practices, contributing to overall energy efficiency. This integration not only reduces the carbon footprint of construction projects but also aligns with global sustainability goals. The impact of AI on boosting renewable energy utilization and visual power plant efficiency is profound. By leveraging AI technologies, contemporary construction can achieve higher energy efficiency, lower operational costs, and increased sustainability. As AI continues to evolve, its role in the renewable energy sector will likely expand, further enhancing the capabilities of renewable energy systems and paving the way for a more sustainable future in construction and beyond. This paper underscores the transformative potential of AI in renewable energy and its critical role in shaping the future of sustainable construction practices.

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.

Concurrent PV production and consumption load forecasting using CT‐Transformer deep learning to estimate energy system flexibility

AbstractThe integration of renewable energy sources (RESs) into power systems has increased significantly due to technical, economic, and environmental factors, necessitating greater flexibility to manage variable consumption loads and renewable energy generation. Accurate forecasting of solar energy production and consumption load is critical for enhancing power system flexibility. This study introduces a novel deep learning model, a spatial‐temporal hybrid convolutional‐transformer (CT‐Transformer) network with unique features and extended memory capacity. Additionally, a flexibility index (FI) is introduced to evaluate power system flexibility (PSF) based on the forecasting results. The CT‐Transformer forecasts PSF for the next 24 and 168 hours, using the FI to evaluate PSF based on forecasting results. The input data includes meteorological, solar energy production, load demand, and pricing data from France, comprising hourly data from 2015 and 2016 (about 17,520 entries). Data preprocessing involves correcting incomplete and irrelevant segments. The CT‐Transformer's performance is compared to other deep learning techniques, showing superior results with the lowest prediction error (2.5%) and a maximum error of 10.1% (MAE). It also achieved a prediction error of 0.08% for system flexibility, compared to the highest error of 0.96%. This research highlights the CT‐Transformer's potential for accurate RES and load forecasting and PSF evaluation.

Methods and algorithms of swarm intelligence for the problems of nonlinear regression analysis and optimization of complex processes, objects, and systems: review and modification of methods and algorithms

The development of high-speed methods and algorithms for global multidimensional optimization and their modifications in various fields of science, technology, and economics is an urgent problem that involves reducing computing costs, accelerating, and effectively searching for solutions to such problems. Since most serious problems involve the search for tens, hundreds, or thousands of optimal parameters of mathematical models, the search space for these parameters grows non-linearly. Currently, there are many modern methods and algorithms of swarm intelligence that solve today's scientific and applied problems, but they require modifications due to the large spaces of searching for optimal model parameters. Modern swarm intelligence has significant potential for application in the energy industry due to its ability to optimize and solve complex problems. It can be used to solve scientific and applied problems of optimizing energy consumption in buildings, industrial complexes, and urban systems, reducing energy losses, and increasing the efficiency of resource use, as well as for the construction of various elements of energy systems in general. Well-known methods and algorithms of swarm intelligence are also actively applied to forecast energy production from renewable sources, such as solar and wind energy. This allows better management of energy sources and planning of their use. The relevance of modifications of methods and algorithms is due to the issues of speeding up their work when solving machine learning problems, in particular, in nonlinear regression models, classification, and clustering problems, where the number of observed data can reach tens and hundreds of thousands or more. The work considers and modifies well-known effective methods and algorithms of swarm intelligence (particle swarm optimization algorithm, bee optimization algorithm, differential evolution method) for finding solutions to multidimensional extremal problems with and without restrictions, as well as problems of nonlinear regression analysis. The obtained modifications of the well-known classic effective methods and algorithms of swarm intelligence, which are present in the work, effectively solve complex scientific and applied tasks of designing complex objects and systems. A comparative analysis of methods and algorithms will be conducted in the next study on this topic. Keywords: optimization, swarm intelligence, mathematical modelling, nonlinear regression, complex objects and systems.

ПЕРСПЕКТИВИ ЗАСТОСУВАННЯ РЕКУРЕНТНОГО АНАЛІЗУ ДЛЯ ЦИКЛІЧНИХ ПРОЦЕСІВ У ВИРОБНИЦТВІ ЕЛЕКТРОЕНЕРГІЇ

The increasing demand for flexible and dynamic power systems drives the search for new modeling and forecasting methods. Recurrent analysis, which has gained wide recognition in natural language processing and machine learning, has the potential to become a promising tool for research and optimization of cyclic processes in electricity generation. This paper explores the potential applications of recurrent analysis for addressing various energy-related tasks including electricity demand forecasting, dispatch optimization, power plant scheduling, and consumption data analysis. The paper discusses the advantages of recurrent analysis, such as flexibility, learning ability, and high accuracy, as well as the challenges associated with large data volumes, computational resources, and result interpretation. It emphasizes the prospects of recurrent analysis for developing more efficient, reliable, and sustainable power systems. The article also explores the integration of recurrence analysis with other data-driven techniques such as machine learning and statistical modeling. This integration enhances the predictive capabilities of recurrence analysis, allowing for more accurate and reliable forecasts of energy production and demand cycles. The synergy between recurrence analysis and machine learning algorithms can lead to the development of advanced control systems that dynamically adjust to changing conditions in real-time, thus ensuring a more stable and efficient power supply. In conclusion, the article posits that recurrence analysis holds significant potential for enhancing the understanding and management of cyclic processes in power generation. By providing detailed insights into the temporal dynamics of energy production systems, recurrence analysis can contribute to more efficient and reliable power generation, ultimately supporting the transition to sustainable energy systems. The adoption of recurrence analysis in the energy sector represents a step forward in leveraging advanced mathematical techniques to address the complexities of modern power generation.

Short-Term Forecast of Photovoltaic Solar Energy Production Using LSTM

In recent times, renewable energy sources have gained considerable vitality due to their inexhaustible resources and the detrimental effects of fossil fuels, such as the impact of greenhouse gases on the planet. This article aims to be a supportive tool for the development of research in the field of artificial intelligence (AI), as it presents a solution for predicting photovoltaic energy production. The basis of the AI models is provided from two data sets, one for generated electrical power and another for meteorological data, related to the year 2017, which are freely available on the Energias de Portugal (EDP) Open Project website. The implemented AI models rely on long short-term memory (LSTM) neural networks, providing a forecast value for electrical energy with a 60-min horizon based on meteorological variables. The performance of the models is evaluated using the performance indicators MAE, RMSE, and R2, for which favorable results were obtained, with particular emphasis on forecasts for the spring and summer seasons.

Understanding the complex dynamics of climate change in BRICS countries: Analyzing the COP26 and COP27 agendas

The urgency to address climate change becomes increasingly evident as we observe a rise in devastating natural disasters and significant changes in global temperatures. This comprehensive study critically assesses the adherence to climate targets set at COP26 and COP27, employing a dual approach encompassing theoretical and empirical aspects—basic and additional analysis. According to theoretical findings, China, Brazil, and South Africa are still experiencing an increase in climate change indicators despite their collective efforts. Notably, Brazil has shown limited progress in green financing initiatives. Moving to an empirical analysis covering 1995–2021, the study employs advanced econometric techniques, including panel ARDL, FMOLS, DOLS, CS-ARDL, and Grey forecasting models (GM (1,1) and DGM (1,1)). The past data on energy production using both renewable and non-renewable sources spanning from 2010 to 2021 to forecast energy production for the next 8 years, extending up to 2029. Results indicate that green financing, renewable energy consumption, natural resource rents, and government effectiveness significantly reduce GHG emissions. Conversely, economic growth, including its cubic form, exacerbates GHG emission trends. Moreover, the study validates the environmental N-shaped hypothesis in the examined countries, providing a complete understanding of climate change's intricate and multifaceted impacts. The grey forecasting model shows that Brazil, Russia, and South Africa are actively endeavoring to curb greenhouse gas emissions by transitioning toward renewable energy sources for energy production. This research contributes valuable insights for policymakers, emphasizing the importance of targeted interventions in green financing and sustainable practices to effectively address and mitigate climate change.

Probabilistic modeling of renewable energy sources in smart grids: A stochastic optimization perspective

Machine learning (ML) techniques have emerged as powerful tools for optimizing renewable energy management in smart grids. This paper focuses on the application of ML algorithms to enhance the efficiency and reliability of renewable energy integration within smart grid systems. By leveraging predictive analytics, ML models can forecast energy production and consumption patterns, facilitating proactive decision-making for grid operators and energy stakeholders. The abstract explores various ML methodologies such as supervised learning, unsupervised learning, and reinforcement learning, tailored to address specific challenges in renewable energy management. Supervised learning algorithms enable accurate prediction of renewable energy generation, aiding in resource allocation and demand-response strategies. Unsupervised learning techniques facilitate anomaly detection and clustering of energy consumption patterns, contributing to grid stability and optimization. Reinforcement learning algorithms optimize control strategies, enabling autonomous decision-making in dynamic grid environments. The abstract highlights case studies and real-world implementations where ML techniques have demonstrated significant improvements in renewable energy integration, grid reliability, and cost-effectiveness. Through a synthesis of research findings and practical insights, this abstract elucidates the transformative potential of machine learning in shaping the future of smart grids and renewable energy management.

Integrating Machine Learning and MLOps for Wind Energy Forecasting: A Comparative Analysis and Optimization Study on Türkiye’s Wind Data

This study conducted a detailed comparative analysis of various machine learning models to enhance wind energy forecasts, including linear regression, decision tree, random forest, gradient boosting machine, XGBoost, LightGBM, and CatBoost. Furthermore, it developed an end-to-end MLOps pipeline leveraging SCADA data from a wind turbine in Türkiye. This research not only compared models using the RMSE metric for selection and optimization but also explored in detail the impact of integrating machine learning with MLOps on the precision of energy production forecasts. It investigated the suitability and efficiency of ML models in predicting wind energy with MLOps integration. The study explored ways to improve LightGBM algorithm performance through hyperparameter tuning and Docker utilization. It also highlighted challenges in speeding up MLOps development and deployment processes. Model performance was assessed using the RMSE metric, conducting a comparative evaluation across different models. The findings revealed that the RMSE values among the regression models ranged from 460 kW to 192 kW. Focusing on enhancing LightGBM, the research decreased the RMSE value to 190.34 kW. Despite facing technical and operational hurdles, the implementation of MLOps was proven to enhance the speed (latency of 9 ms), reliability (through Docker encapsulation), and scalability (using Docker swarm) of machine learning endeavors.

On Integrating Time-Series Modeling with Long Short-Term Memory and Bayesian Optimization: A Comparative Analysis for Photovoltaic Power Forecasting

The means of energy generation are rapidly progressing as production shifts from a centralized model to a fully decentralized one that relies on renewable energy sources. Energy generation is intermittent and difficult to control owing to the high variability in the weather parameters. Consequently, accurate forecasting has gained increased significance in ensuring a balance between energy supply and demand with maximum efficiency and sustainability. Despite numerous studies on this issue, large sample datasets and measurements of meteorological variables at plant sites are generally required to obtain a higher prediction accuracy. In practical applications, we often encounter the problem of insufficient sample data, which makes it challenging to accurately forecast energy production with limited data. The Holt–Winters exponential smoothing method is a statistical tool that is frequently employed to forecast periodic series, owing to its low demand for training data and high forecasting accuracy. However, this model has limitations, particularly when handling time-series analysis for long-horizon predictions. To overcome this shortcoming, this study proposes an integrated approach that combines the Holt–Winters exponential smoothing method with long short-term memory and Bayesian optimization to handle long-range dependencies. For illustrative purposes, this new method is applied to forecast rooftop photovoltaic production in a real-world case study, where it is assumed that measurements of meteorological variables (such as solar irradiance and temperature) at the plant site are not available. Through our analysis, we found that by utilizing these methods in combination, we can develop more accurate and reliable forecasting models that can inform decision-making and resource management in this field.

Prediction of Electricity Generation Using Onshore Wind and Solar Energy in Germany

Renewable energy production is one of the most important strategies to reduce the emission of greenhouse gases. However, wind and solar energy especially depend on time-varying properties of the environment, such as weather. Hence, for the control and stabilization of electricity grids, the accurate forecasting of energy production from renewable energy sources is essential. This study provides an empirical comparison of the forecasting accuracy of electricity generation from renewable energy sources by different deep learning methods, including five different Transformer-based forecasting models based on weather data. The models are compared with the long short-term memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models as a baseline. The accuracy of these models is evaluated across diverse forecast periods, and the impact of utilizing selected weather data versus all available data on predictive performance is investigated. Distinct performance patterns emerge among the Transformer-based models, with Autoformer and FEDformer exhibiting suboptimal results for this task, especially when utilizing a comprehensive set of weather parameters. In contrast, the Informer model demonstrates superior predictive capabilities for onshore wind power and photovoltaic (PV) power production. The Informer model consistently performs well in predicting both onshore wind and PV energy. Notably, the LSTM model outperforms all other models across various categories. This research emphasizes the significance of selectively using weather parameters for improved performance compared to employing all parameters and a time reference. We show that the suitability and performance of a prediction model can vary significantly, depending on the specific forecasting task and the data that are provided to the model.

Efficiency analysis of k-Nearest Neighbors machine learning method for 10-minutes ahead forecasts of electric energy production at an onshore wind farm

Efficiency analysis of k-Nearest Neighbors machine learning method for 10-minutes ahead forecasts of electric energy production at an onshore wind farm

Controlled power distributed photovoltaic system using solar energy forecast

The use of renewable energy sources to supply electricity in customer grids is a current practise in developed countries and, particularly, in the case of distributed solar energy generation grids. Even though, the environmental benefits of the practise drive to economic incentives to increase the renewable energies. However, because of the unsteady behaviour of the renewable electric energy sources [1], as wind energy and solar photovoltaic production, the increment in the use of these sources produces severe fluctuations in the electric grids. Then, their electric supply to the electric grid is limited and at the same time, an accurate renewable energy production forecast is required. In this work, the feasibility of a new approach to reduce this problem is tested by the application (on a yearly basis) of a simulator of a production-storage controlled system, namely FOTOV, to be applied in the design of a photovoltaic (PV) installation

Effect of Saharan dust episodes on the accuracy of photovoltaic energy production forecast in Hungary (Central Europe)

In order to meet global sustainability goals, in particular, the rapid decarbonisation of the energy sector in combination with geopolitical energy security issues, as well as further improvement in regional air quality-the so-called renewable energy sources are becoming increasingly critical and important. Photovoltaic power (PV) generation is clearly the most widely deployed (non-hydro) renewable energy source globally, which still has a significant growth potential in many countries, including Hungary.However, due to the intermittent nature and the strong dependency of photovoltaic power production on meteorological factors, continuous adjustment of electric power in the electric grid is needed. This, in turn, requires the most accurate forecast of the photovoltaic energy to be produced in the ultra-short term (15 min) and short term (1 day) in order to minimise last-minute and high-price energy acquisition or unplanned kicks-in of gas-fired auxiliary power plants.In this paper, we evaluate the impacts of large-scale dust transport episodes on the accuracy of forecasts of PV power generation. The numbers and intensities of Saharan dust storm events reaching Central Europe have increased over the last decade, hitting a new record in 2022 with 16 (!) African dust episodes observed over Hungary. We have shown that the semi-direct effect of atmospheric dust particles on high-level cloud formation rather than their direct irradiance-reducing effect is responsible for the reduced accuracies of e short-term (24-h) PV energy production forecasts during these events.

Prediction of energy production in a building-integrated photovoltaic system using machine learning algorithms

Globally, the building sector is a significant consumer of energy. Implementing effective energy management system (EMS) strategies is crucial for reducing energy consumption and optimizing energy usage across all building elements. Moreover, solar energy is increasingly recognized as essential for sustainable building and urban development. Thus, the energy production forecast from photovoltaic systems installed on buildings is pivotal for a smart EMS's realization and optimal functioning. By incorporating accurate energy production forecasts, we can optimize energy consumption by fitting consumption patterns with production peaks and valleys. This study presents a comparative analysis of five widely used machine learning regressing models, including Random Forest, Decision Trees, XGBoost, LightGBM, and CatBoost for predicting the hourly energy production from a PV system mounted on a building. In this investigation, weather data and time-related characteristics were utilized as predictors. The results showed that LightGBM outperformed the other models, proving its suitability for PV energy forecasting tasks.

Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems

Climate change demands clean energy solutions, and renewable sources such as solar and wind are prime candidates. However, their variability poses challenges for their integration into large-scale power systems. This paper addresses this issue by proposing a novel hybrid mathematical model. The proposal integrates both fossil and renewable sources, considering real-world constraints such as system demand, reserves, and transmission dynamics. The model combines several approaches. By using a novel block composition technique, the computational complexity is reduced, making the model applicable to large-scale systems. A neural network is also developed to improve the forecasting of renewable energy production, which is crucial for managing its intermittency. The effectiveness of the proposed model is tested by considering the large Argentinean electricity system, demonstrating its practical applicability. The results show that acceptable forecasts can be obtained for the generation and transmission scheduling of the whole system.

Forecasting energy production of a PV system connected by using NARX neural network model

Forecasting energy production of a PV system connected by using NARX neural network model

Forecasting Wind and Solar Energy Production in the Greek Power System using ANN Models

Renewable energy sources (RES) like solar and wind are quite uncertain because of the unpredictable nature of wind and sunlight. As a result, there are at present several issues with system security and the transformed structure of the energy market due to the increasing utilization of renewable energy sources (wind and solar). Accurate forecasting of renewable energy production is extremely important to ensure that the produced energy is equal to the consumed energy. Any deviations have an impact on the system's stability and could potentially cause a blackout in some situations. The issue of the high penetration of RES is discussed in this study along with a novel method of predicting them using artificial neural networks (ANN). The SARIMA prediction model is contrasted with the ANN approach. The suggested ANN for wind power plants has a mean average prediction error (MAPE) of 3%–4.3%, whereas the SARIMA model has a MAPE of 5%–6.5%. In comparison, the present prediction approaches typically have a MAPE of 5%–10%. When the MAPE of solar power plants was calculated, it was also discovered that the SARIMA model had a MAPE of 2.3%–4% and the suggested ANN had a MAPE of 1.4%–2.3%, whereas the MAPE of the present prediction methods was often about 9%.