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The purpose of this study is to propose a new magnetic gearing device and the proposed transmission model can be applied in the field of wind power and wave energy generation gearboxes.

A novel radial differential field-modulated two-stage magnetic gear is introduced, using a unique radial differential linkage to integrate two single-stage magnetic gears. This design incorporates a magnetic isolation ring to enhance transmission efficiency by minimizing magnetic interference and preventing power circulation. The two-stage modulating ring rotor operates synchronously via a connecting bridge, ensuring system stability and efficiency. This configuration not only boosts the gear ratio but also maintains a compact structure, improving power density and efficiency. Leveraging the magnetic field modulation and differential transmission, a finite element model of this gear is developed and its electromagnetic performance is analyzed.

The torque density of the new radial differential field-modulated two-stage magnetic gear has increased by 44.97% compared to the traditional tandem two-stage magnetic gear. It achieves a high transmission ratio of 64 and maintains comparable power density, indicating strong torque transfer capabilities suitable for low-speed, high-torque applications. During steady-state operation, the torque pulsation difference between the two stages is minimal, ensuring stable working torque.

This research not only propels the forefront of magnetic gear technology through heightened efficiency and streamlined design but also bears profound societal significance in fostering sustainable energy paradigms. By facilitating superior energy conversion efficiencies in wind turbines and wave energy converters, it plays a pivotal role in mitigating carbon emissions and accelerating the global pivot towards cleaner, renewable energy landscapes.

A magnetic gear transmission device is proposed, which can achieve high power density and large transmission ratio at the same time, and this study provides a useful reference for the design optimization of new high-performance multistage magnetic gears.

This study aims to conduct the domain mapping of consumer behaviour research in the context of solar energy. The study can help in understanding the intellectual structure, evolution of keywords and key research producers (at the author, institutional and source level) related to the domain of solar energy consumer research.

This study uses R-studios’ bibliometrix package for analysing the bibliographical data collected from the Scopus database. Analysis has been conducted at the descriptive level (summary, author, institution and source) and analytical level (co-citation analysis, co-occurrence analysis, thematic maps and historiography).

This study finds out the most relevant authors, institutions and sources using criteria such as production, citations and H-index. Relevant research clusters have been identified using the clustering of authors, co-citations and keywords. Thematic mapping has identified the basic and motor themes. Historical citation analysis shows the direct linkage of previous studies. Overall, this study reports the most relevant bibliometric indicators in the domain of solar energy consumer research.

Identified patterns can help policymakers, business experts, social marketers and energy conservation organisations to study consumer behaviour.

Thiis bibliometric study can effectively assess sustainable development goals and suggest improved action plans.

This study examined bibliometric analysis in solar energy products (SEPs), recognised varied domains of research work on consumers’ intention to purchase solar household products and mapped them into six groups. This study provides an overview of 40 years of research on consumer behaviour towards SEPs and discusses its findings to identify the research gap.

The energy generation process through photovoltaic (PV) panels is contingent upon uncontrollable variables such as wind patterns, cloud cover, temperatures, solar irradiance intensity and duration of exposure. Fluctuations in these variables can lead to interruptions in power generation and losses in output. This study aims to establish a measurement setup that enables monitoring, tracking and prediction of the generated energy in a PV energy system to ensure overall system security and stability. Toward this goal, data pertaining to the PV energy system is measured and recorded in real-time independently of location. Subsequently, the recorded data is used for power prediction.

Data obtained from the experimental setup include voltage and current values of the PV panel, battery and load; temperature readings of the solar panel surface, environment and the battery; and measurements of humidity, pressure and radiation values in the panel’s environment. These data were monitored and recorded in real-time through a computer interface and mobile interface enabling remote access. For prediction purposes, machine learning methods, including the gradient boosting regressor (GBR), support vector machine (SVM) and k-nearest neighbors (k-NN) algorithms, have been selected. The resulting outputs have been interpreted through graphical representations. For the numerical interpretation of the obtained predictive data, performance measurement criteria such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and R-squared (R²) have been used.

It has been determined that the most successful prediction model is k-NN, whereas the prediction model with the lowest performance is SVM. According to the accuracy performance comparison conducted on the test data, k-NN exhibits the highest accuracy rate of 82%, whereas the accuracy rate for the GBR algorithm is 80%, and the accuracy rate for the SVM algorithm is 72%.

The experimental setup used in this study, including the measurement and monitoring apparatus, has been specifically designed for this research. The system is capable of remote monitoring both through a computer interface and a custom-developed mobile application. Measurements were conducted on the Karabük University campus, thereby revealing the energy potential of the Karabük province. This system serves as an exemplary study and can be deployed to any desired location for remote monitoring. Numerous methods and techniques exist for power prediction. In this study, contemporary machine learning techniques, which are pertinent to power prediction, have been used, and their performances are presented comparatively.