Search Results (12)

Data-driven forecasting is becoming increasingly central to modern energy management, yet nonspecialists without a background in artificial intelligence (AI) face significant barriers to entry. While Python is the dominant machine learning language, R remains a practical and accessible tool for users with expertise in statistics, engineering, or domain-specific analysis. To inform tool selection, we first provide an evidence-based comparison of R with major alternatives before reviewing 49 peer-reviewed articles published between 2020 and 2025 in Science Citation Index Expanded (SCIE)-level journals that utilized R for energy forecasting tasks, including electricity (regional and site-level), solar, wind, thermal energy, and natural gas. Despite such growth, the field still lacks a systematic, cross-domain synthesis that clarifies which R-based methods prevail, how accessible workflows are implemented, and where methodological gaps remain; this motivated our use of text mining. Text mining techniques were employed to categorize the literature according to forecasting objectives, modeling methods, application domains, and tool usage patterns. The results indicate that tree-based ensemble learning models—e.g., random forests, gradient boosting, and hybrid variants—are employed most frequently, particularly for solar and short-term load forecasting. Notably, few studies incorporated automated model selection or explainable AI; however, there is a growing shift toward interpretable and beginner-friendly workflows. This review offers a practical reference for nonexperts seeking to apply R in energy forecasting contexts, emphasizing accessible modeling strategies and reproducible practices. We also curate example R scripts, workflow templates, and a study-level link catalog to support replication. The findings of this review support the broader democratization of energy analytics by identifying trends and methodologies suitable for users without advanced AI training. Finally, we synthesize domain-specific evidence and outline the text-mining pipeline, present visual keyword profiles and comparative performance tables that surface prevailing strategies and unmet needs, and conclude with practical guidance and targeted directions for future research. Full article

The unpredictable nature of renewable energy sources, such as wind and solar, makes them unreliable sources of energy for the power system. Nevertheless, with the advancement in the field of artificial intelligence (AI), one can predict the availability of solar and wind energy in the short, medium, and long term with fairly high accuracy. As such, this research work aims to develop a machine-learning-based framework for forecasting global horizontal irradiance (GHI) for Muscat, Oman. The proposed framework includes a data preprocessing stage, where the missing entries in the acquired data are imputed using the mean value imputation method. Afterward, data scaling is carried out to avoid the overfitting/underfitting of the model. Features such as the GHI cloudy sky index, the GHI clear sky index, global normal irradiance (GNI) for a cloudy sky, GNI for a clear sky, direct normal irradiance (DNI) for a cloudy sky, and DNI for a clear sky are extracted. After analyzing the correlation between the abovementioned features, model training and the testing procedure are initiated. In this research, different models, named Linear Regression (LR), Support Vector Machine (SVR), KNN Regressor, Decision Forest Regressor, XGBoost Regressor, Neural Network (NN), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Random Forest Regressor, Categorical Boosting (CatBoost), Deep Autoregressive (DeepAR), and Facebook Prophet, are trained and tested under both identical features and a training–testing ratio. The model evaluation metrics used in this study include the mean absolute error (MAE), the root mean squared error (RMSE), R², and mean bias deviation (MBD). Based on the outcomes of this study, it is concluded that the Facebook Prophet model outperforms all of the other utilized conventional machine learning models, with MAE, RMSE, and R² values of 9.876, 18.762, and 0.991 for the cloudy conditions and 11.613, 19.951 and 0.988 for the clean weather conditions, respectively. The mentioned error values are the lowest among all of the studied models, which makes Facebook Prophet the most accurate solar irradiance forecasting model for Muscat, Oman. Full article

The intermittent nature of renewable energy sources (RES) restricts their widespread applications and reliability. Nevertheless, with advancements in the field of artificial intelligence, we can predict the variations in parameters such as wind speed and solar irradiance for the short, medium and long terms. As such, this research attempts to develop a machine learning (ML)-based framework for predicting solar irradiance at Muscat, Oman. The developed framework offers a methodological way to choose an appropriate machine learning model for long-term solar irradiance forecasting using Python’s built-in libraries. The five different methods, named linear regression (LR), seasonal autoregressive integrated moving average with exogenous variables (SARIMAX), support vector regression (SVR), Prophet, k-nearest neighbors (k-NN), and long short-term memory (LSTM) network are tested for a fair comparative analysis based on some of the most widely used performance evaluation metrics, such as the mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R²) score. The dataset utilized for training and testing in this research work includes 24 years of data samples (from 2000 to 2023) for solar irradiance, wind speed, humidity, and ambient temperature. Before splitting the data into training and testing, it was pre-processed to impute the missing data entries. Afterward, data scaling was conducted to standardize the data to a common scale, which ensures uniformity across the dataset. The pre-processed dataset was then split into two parts, i.e., training (from 2000 to 2019) and testing (from 2020 to 2023). The outcomes of this study revealed that the SARIMAX model, with an MSE of 0.0746, MAE of 0.2096, and an R² score of 0.9197, performs better than other competitive models under identical datasets, training/testing ratios, and selected features. Full article

Burial stone relics remain in a humid, semi-enclosed environment for long periods, and temperature and humidity variations can cause deterioration acceleration. Yang Can’s tomb was selected as the research object, and field monitoring and simulations were performed to investigate the characteristics of temperature and humidity variations, after which the simulation results were evaluated. The monitoring results showed that solar radiation, rainfall, wind speed, and depth of entry are important factors affecting the variation in the temperature and humidity of burial stone relics. The temperature outside the chamber is greatly affected by seasonal variations, while the humidity inside the chamber is influenced by seasonal variations, so appropriate measures should be implemented inside and outside the chamber during different seasons to alleviate deterioration. On the basis of the above analysis, a temperature and humidity model for the interior chamber of burial stone relics was established in COMSOL software 5.6, combined with a porous medium heat transfer model and computational fluid dynamics (CFD) model. The temperature and humidity inside the chamber can be calculated by the temperature and humidity outside the chamber. This study provides data support for hydrothermal, condensation and other related studies of burial stone relics. Full article

The energy sector faces a pressing need for significant transformation to curb CO₂ emissions. For instance, Czechia and Germany have taken steps to phase out fossil thermal power plants by 2038, opting instead for a greater reliance on variable renewable energy sources like wind and solar power. Nonetheless, thermal power plants will still have roles, too. While the conventional multistage axial turbine design has been predominant in large-scale power plants for the past century, it is unsuitable for small-scale decentralized projects due to complexity and cost. To address this, the study investigates less common turbine types, which were discarded as they demonstrated lower efficiency. One design is the Elektra turbine, characterized by its velocity compounded radial re-entry configuration. The Elektra turbine combines the advantages of volumetric expanders (the low rotational speed requirement) with the advantages of a turbine (no rubbing seals, no lubrication in the working fluid, wear is almost completely avoided). Thus, the research goal of the authors is the implementation of a 10 kW-class ORC turbine driving a cost-effective off-the-shelf 3000 rpm generator. The paper introduces the concept of the Elektra turbine in comparison to other turbines and proposes this approach for an ORC working fluid. In the second part, the 1D design and 3D–CFD optimization of the 7 kW Elektra turbine working with Hexamethyldisiloxane (MM) is performed. Finally, CFD efficiency characteristics of various versions of the Elektra are presented and critically discussed regarding the originally defined design approach. The unsteady CFD calculation of the final Elektra version showed 46% total-to-static isentropic efficiency. Full article

In recent years, a large number of countries have connected and distributed photovoltaics in remote rural areas, aiming to promote the use of clean energy in rural areas. The solar energy that is not used in time needs to be discarded, resulting in a large amount of wasted energy. Rural areas are closely related to agricultural production, and solar energy can be used for agricultural nitrogen fixation to supplement the nitrogen needed by crops and effectively use the upcoming waste of solar energy. A photovoltaic-driven plasma reactor for nitrogen fixation in agriculture was designed in this study. The air inlet and outlet holes are arranged above and below the reactor to facilitate air entry and directly interact with the gliding arc generated at the bottom of the electrode to achieve atmospheric nitrogen fixation in agriculture. The characteristics of gliding arc development in the process of nitrogen fixation in agriculture were studied experimentally. There are two discharge modes of the gliding arc discharge: one is steady arc gliding mode (A-G Mode), and the other is breakdown gliding mode (B-G Mode). By collecting discharge signals, different discharge modes of gliding arc discharge were analyzed, and the effect of the air flow rate on the discharge period and discharge mode ratio distribution is discussed. The effects of the air flow rate on the yield, specific energy input, and energy consumption in plasma agriculture were studied. The experimental results show that with an increase in the air flow rate, the B-G mode takes up a larger proportion and the gliding arc discharge period is shortened. However, the higher the proportion of the B-G mode, the more unfavorable the production of nitrogen oxides. Although the nitrogen oxides generated by the system are not particularly excellent compared with the Haber-Bosch ammonia process (H-B process), the access to distributed photovoltaic roofs in rural and remote areas can effectively use available resources like water, air, and solar, and avoid energy waste in areas where wind and solar are abandoned. Full article

Power grids of the future will likely incorporate more renewable energy distributed generation (REDG), also known as alternative energy systems. REDG units are increasingly being used in electrical transmission networks because of the positive effects they have on power networks. REDG systems are the backbone of smart electric networks and are essential to the operation of the smart grid. These REDG systems can additionally improve system reliability by providing some customers with a backup generator in the event of power interruptions. This review offers a thorough evaluation of the existing body of information on the topic of electric vehicles’ (EVs’) future interactions with smart grids. The combination of the potential deployment of EVs and the smart grid’s conceptual goal presents challenges for electric grid-related infra-structure, communication, and control. The proposal for connecting EVs to the grid is based on research into cutting-edge smart metering and communication systems. In the context of the vehicle-to-grid (V2G) phenomenon, the possibilities, benefits, and limitations of various EV smart-charging systems are also fully examined. A quickly growing percentage of distributed energy is derived from wind and solar (photovoltaic) energy. The variable power output of wind and solar energy introduces fresh challenges for those responsible for organizing, operating, and controlling the power grid. While fluctuations in the electric grid are problematic, they may be mitigated by the entry of EVs into the energy market. As such, we performed a comprehensive review of the literature to learn more about this exciting research gap that needs to be filled and to identify recently developed solutions to the problems related to EVs. Additionally, in this review article, we take a close look at the practicality of V2G technology. The smart grid is a developing concept that will likely have large implications for the world’s energy infrastructure, and this study thoroughly analyzes how EVs interact with it. Full article

Under the conditions of climate change and energy crisis stemming from the COVID-19 pandemic and the embargo on the supply of raw materials from Russia, high hopes are attached to the development of renewable energy in terms of meeting energy needs. Still, renewable energy has some drawbacks too. In the most dynamically growing solar and wind energy industries, the main problems that are indicated include this energy storage and ensuring the security of supplies. These are supposed to be solved by the digital transformation of renewable power generation plus the entry of market players that implement digital business models in renewable energy. The purpose of the article is to identify a framework “digital compass” of business models in renewable energy within a group of solar and wind energy start-ups, operating in energy storage and supply industries. At the base of this study there were: digital technologies, customer orientation, delivery of value and revenue stream. The research algorithm applied here enabled the identification and classification of startup business models based on secondary data using R software. The results show that the identified startups implement digital business models to a minor extent. Startups dealing with solar energy storage stand out in a quite positive manner. The low digital attractiveness of investing in wind energy storage and supply (which, to a smaller extent applies to solar energy), is also indicated the investment preferences of big-tech. Thus, the future of the digital transformation of these industries should be related to regulatory changes rather than technological ones. Full article

The energy industry must change dramatically in order to reduce CO₂-emissions and to slow down climate change. Germany, for example, decided to shut down all large nuclear (2022) and fossil thermal power plants by 2038. Power generation will then rely on fluctuating renewables such as wind power and solar. However, thermal power plants will still play a role with respect to waste incineration, biomass, exploitation of geothermal wells, concentrated solar power (CSP), power-to-heat-to-power plants (P2H2P), and of course waste heat recovery (WHR). While the multistage axial turbine has prevailed for the last hundred years in power plants of the several hundred MW class, this architecture is certainly not the appropriate solution for small-scale waste heat recovery below 1 MW or even below 100 kW. Simpler, cost-effective turbo generators are required. Therefore, the authors examine uncommon turbine architectures that are known per se but were abandoned when power plants grew due to their poor efficiency compared to the multistage axial machines. One of these concepts is the so-called Elektra turbine, a velocity compounded radial re-entry turbine. The paper describes the concept of the Elektra turbine in comparison to other turbine concepts, especially other velocity compounded turbines, such as the Curtis type. In the second part, the 1D design and 3D computational fluid dynamics (CFD) optimization of the 5 kW air turbine demonstrator is explained. Finally, experimentally determined efficiency characteristics of various early versions of the Elektra are presented, compared, and critically discussed regarding the originally defined design approach. The unsteady CFD calculation of the final Elektra version promised 49.4% total-to-static isentropic efficiency, whereas the experiments confirmed 44.5%. Full article

Wave energy is one of the most promising renewable energies available with its very large resource. The waves generated by the wind field are steadier than the wind field itself, rendering wave energy more consistent than wind energy. It is also more predictable than wind and solar. Wave energy is making continuous progress towards commercialisation, and thanks to an increasing number of deployments at sea, the sector is increasing the understanding of the costs and economies of these projects. No wave energy converter has been demonstrated to be commercially viable, and it is yet to be proven that wave energy can contribute to the renewable energy mix. In this context, and in order to find an economically viable solution for exploiting wave energy, it is important to assess the economic potential of a particular concept throughout the entire technological development process. At early development stages, this assessment can be challenging and present large uncertainties. Notwithstanding, it is important to perform the economic assessment already at the early stages in order to identify possible bottlenecks or potential improvements or modifications of a concept. This work presents guidance for the economic evaluation of a wave energy concept at an early development stage by setting up the economic frame based on a target LCoE. It involves the understanding of the entry cost to be achieved for a specific target market and evaluating the breakdown of costs based on a detailed technology agnostic database of costs. The guidance is then applied to a new type of wave energy converter, in which the primary coupling with the waves is through hydrodynamic lift forces. Full article

We present a coordinated observation with the Magnetospheric Multiscale (MMS) mission, located in the Earth’s magnetotail plasma sheet, and the Acceleration, Reconnection, Turbulence, and Electrodynamics of the Moon’s Interaction with the Sun (ARTEMIS) mission, located in the solar wind, in order to understand the formation mechanism of the cold and dense plasma sheet (CDPS). MMS detected two CDPSs composed of two ion populations with different energies, where the energy of the cold ion population is the same as that of the solar wind measured by ARTEMIS. This feature directly indicates that the CDPSs are caused by the solar wind entry. In addition, He⁺ was observed in the CDPSs. The plasma density in these two CDPSs are ~1.8 cm⁻³ and ~10 cm⁻³, respectively, roughly 4–30 times the average value of a plasma sheet. We performed a cross-correlation analysis on the ion density of the CDPS and the solar wind, and we found that it takes 3.7–5.9 h for the solar wind to enter the plasma sheet. Such a coordinated observation confirms the previous speculation based on single-spacecraft measurements. Full article

A new type of solar tower was developed through laboratory experiments and numerical analyses. The solar tower mainly consists of three components. The transparent collector area is an aboveground glass roof, with increasing height toward the center. Attached to the center of the inside of the collector is a vertical tower within which a wind turbine is mounted at the lower entry to the tower. When solar radiation heats the ground through the glass roof, ascending warm air is guided to the center and into the tower. A solar tower that can generate electricity using a simple structure that enables easy and less costly maintenance has considerable advantages. However, conversion efficiency from sunshine energy to mechanical turbine energy is very low. Aiming to improve this efficiency, the research project developed a diffuser-type tower instead of a cylindrical tower, and investigated a suitable diffuser shape for practical use. After changing the tower height and diffuser open angle, with a temperature difference between the ambient air aloft and within the collector, various diffuser tower shapes were tested by laboratory experiments and numerical analyses. As a result, it was found that a diffuser tower with a semi-open angle of 4° is an optimal shape, producing the fastest updraft at each temperature difference in both the laboratory experiments and numerical analyses. The relationships between thermal updraft speed and temperature difference and/or tower height were confirmed. It was found that the thermal updraft velocity is proportional to the square root of the tower height and/or temperature difference. Full article