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This paper aims to develop a causal feedback structure that explains the dynamics of entrepreneurship development in Iran’s photovoltaic (PV) technological innovation system (TIS) to design effective policy interventions for fostering PV innovation.

This study adopts the system dynamics approach to develop the causal structure model. The methodology follows a systematic method to elicit the causal structure from qualitative data gathered by interviewing several stakeholders with extensive knowledge about different aspects of Iran’s PV TIS.

Lack of technological knowledge and financial resources within Iranian PV panel-producing firms are the main barriers to entrepreneurship development in Iran’s PV TIS. This study proposes two policy enforcement mechanisms to tackle these problems. The proposed feedback mechanisms contribute to the domestic PV market size and knowledge transfer from public research organizations to the PV industry.

The proposed policy mechanisms aid Iranian policymakers in designing effective policy interventions stimulating innovation in Iran’s PV industry.

The main contributions of this study include conceptualizing the causal structure capturing entrepreneurship dynamics in emerging PV TIS and proposing policy mechanisms fostering entrepreneurship and innovation in PV sectors.

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.

This study investigates the barriers and drivers of using green methods and technologies (GMTs) in supportive educational buildings (SEBs) in South Africa, and assesses their impact on the circular economy (CE) in achieving net-zero carbon goals. While there has been extensive literature on green building technologies, there is limited research on the barriers and drivers of using GMT in SEBs, as well as their impact on the circular economy (CE) in achieving net-zero carbon goals.

This study adopts an interpretivist approach with an ontological basis, using an overarching case study of a SEB at the University of Cape Town (UCT). Semistructured interviews were conducted with executive UCT management, and a field survey of a UCT supportive education building was performed.

At UCT, multiple GMTs have been installed across various buildings to enhance monitoring and management of water and energy consumption. Moreover, initiatives to positively influence student behavior, such as water and energy-saving campaigns around UCT premises, have been introduced. The findings further indicate that UCT has recently emphasized the implementation of GMTs, resulting in improved resource efficiency, CE practices and progress toward achieving net-zero carbon targets for supportive education buildings and the university as a whole.

This research highlights the positive impact of GMTs on a SEB’s CE and net-zero carbon operations. As a result, facility managers should consider incorporating GMTs when planning the development or refurbishment of SEBs.

The aim of this research study is to mitigate shading impact on solar photovoltaic array. Photovoltaic (PV) array when getting shaded not only results in appreciable power loss but also exhibits multiple power peaks. Due to these multiple power peaks, the maximum power point tracking (MPPT) controllers’ performance will be affected, as most of the times it ends up in tracking the local maximum power peak and not the global power peak.

The PV panels in an PV array when getting shaded even partially would result in huge power loss. The pattern of shading also plays a crucial role, as it renders a cascaded impact on the overall power output because the cells/panels are connected in series and are parallel. Therefore, during shading, intelligent schemes are needed to appropriately connect and discard the unhealthy and healthy panels in right place with right combination. This research proposes one such scheme to mitigate the shading impact.

To mitigate the shading impact and also to have a smooth power-voltage (P-V) curve, a new series inducing switching scheme is introduced. The proposed scheme not only mitigates the shading impact and enhances the output power but also smoothens the P-V curve that facilitates the MPPTs to track the P-V appropriately.

The research findings are inventive in nature and not copied work. The reference works and the inspirations have been duly cited and credited.

The purpose of this study is optimization of existing PV system and by making the optimization to reach the heights energy performance from the system.

The methodology used in this work is analytical as well as software using PV*SOL premium software. Both methods are used to achieve a more realistic analysis of the results achieved at the end of the work.

After analyzing the optimization of the PV system in terms of certain atmospheric conditions, it is clear that the optimization of the system is necessary. Through the optimization of the systems, a better performance of the system is achieved, as well as in the case in question, it affects the increase of the energy generated annually up to 500 kWh.

This work is the original work of the author, which represents a part of the topic of the doctorate.

Photovoltaic (PV) systems are experiencing exponential growth due to environmental concerns, unlimited and ubiquitous solar energy, and starting-to-make-sense panel costs. Alongside designing more efficient solar panels, installing solar trackers and special circuitry for optimizing power delivery to the load according to a maximum power point tracking (MPPT) algorithm are other ways of increasing efficiency. However, it is critical for any efficiency increase to account for the power consumption of any amendments. Therefore, this paper aims to propose a novel tracker while using MPPT to boost the PV system's actual efficiency accounting for the involved costs.

The proposition is an experimental pneumatic dual-axis solar tracker using light-dependent resistor (LDR) sensors. Due to its embedded energy storage, the pneumatic tracker offers a low duty-cycle operation leading to tracking energy conservation, fewer maintenance needs and scalability potential. While MPPT assures maximum load power delivery, the solar PV's actual delivered power is calculated for the first time, accounting for the solar tracking and MPPT power costs.

The experiments' results show an increase of 37.6% in total and 35.3% in actual power production for the proposed solar tracking system compared to the fixed panel system, with an MPPT efficiency of 90%. Thus, the pneumatic tracking system offers low tracking-energy consumption and good actual power efficiency. Also, the newly proposed pneumatic stimulant can significantly simplify the tracking mechanism and benefit from several advantages that come along with it.

To the best of the authors’ knowledge, this work proposes, for the first time, a single-motor pneumatic dual-axis tracker with less implementation cost, less frequent operation switching and scalability potential, to be developed in future works. Also, the pneumatic proposal delivers high actual power efficiency for the first time to be addressed.

The malfunction variables of power stations are related to the areas of weather, physical structure, control and load behaviour. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behaviour into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a data set. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid, there is a testbed ready to estimate the future failures.

The purpose of this paper is to evaluate the reliability and signature reliability of solar panel k-out-of-n-multiplex system with the help of universal generating function.

Energy scarcity and global warming issues have become important concerns for humanity in recent decades. To solve these problems, various nations work for renewable energy sources (RESs), including sun, breeze, geothermal, wave, radioactive and biofuels. Solar energy is absorbed by solar panels, referred to as photovoltaic panels, which then transform it into electricity that can be used to power buildings or residences. Remote places can be supplied with electricity using these panels. Solar energy is often generated using a solar panel that is connected to an inverter for power supply. As a result, a converter reliability evaluation is frequently required. This paper presents a study on the reliability analysis of k-out-of-n systems with heterogeneous components. In this research, the universal generating function methodology is used to identify the reliability function and signature reliability of the solar array components. This method is commonly used to assess the tail signature and Barlow-Proschan index with independent and identically distributed components.

The Barlow-Proschan index, tail signature, signature, expected lifetime, expected cost and minimal signature of independent identically distributed are all computed.

This is the first study of solar panel k-out-of-n-multiplex systems to examine the signature reliability with the help of universal generating function techniques with various measures.

This paper aims to develop a selective energy optimisation of the photovoltaic–thermal (PV/T) system performance. The PV cell inside the PV/T system could be periodically manipulated to produce domestic hot water without applying an external power supply.

A numerical simulation model of the proposed PV/T model was developed in MATLAB/Simulink to analyse the selective energy optimisation of the model. The extrinsic cell resistance (R_se) is adjusted to control the ratio of thermal to the electrical energy, generated from the PV cell inside the PV/T system. Therefore, the internal heat of the PV cell inside the PV/T system is periodically used as a thermal element to produce electrical power and hot water.

The optimisation of PV/T energy shows that the electrical power efficiency can increase by 11.6% when R_se was 0 Ω, and the 200 L water tank temperature increased by 22ºC when R_se was 50 Ω.

This study showed that the use of the PV cell could be extended to domestic hot water and space heating, and not only for electricity.

The main purpose of this controller is to carryout irrigation by the farmers with renewable energy resources.

The proposed design includes the Deep learning based intelligent stand-alone energy management system used for irrigation purpose. The deep algorithm applied here is Radial basis function neural network which tracks the maximum power, maintains the battery as well as load system.

The Radial Basis Function Neural Network algorithm is used for carrying out the training process. In comparison with other conventional algorithms, this algorithm outperforms by higher efficiency and lower tracking time without oscillation.

It is little complex to implement the hardware setup of neural network in terms of training process but the work is under progress.

The practical hardware implementation is under progress.

If controller are implemented in a real-time environment, definitely it helps the human-less farming and irrigation process.

If this system is implemented in real-time environment, every farmer gets benefitted.