Optimizing solar energy-based energy consumption in atrium-designed residential buildings: An economic, environmental, and energy-centric evaluation

Abstract This study examines the determinants of solar energy adoption in Australia between 1991 and 2023 using an Autoregressive Distributed Lag (ARDL) model. The results reveal long-run equilibrium relationships and short-run adjustment mechanisms between solar deployment and the most pertinent economic, environmental, and financial factors. The results reveal some significant findings. First, financial globalization shows a strong negative long-run effect on Solar Energy Consumption. (SEC), suggesting that international capital flows have previously endorsed fossil fuel systems rather than renewable transitions. Second, coal consumption illustrates a strong negative effect, confirming path dependency challenges. Thirdly, while CO 2 emissions and GDP per capita have a positive effect on SEC, population growth has a negative relationship, suggesting that urbanization may precede the establishment of renewable infrastructure. The study’s policy implications underscore three strategic priorities: accelerating the transition away from coal and toward clean energy systems, transforming financial markets to encourage green investment, and channeling economic growth to build sustainable energy infrastructure. These are the proposals that have been advocated to address structural challenges for Australia’s energy transition while taking advantage of the strengths that have been built. The research presents empirical evidence for integrated policy strategies in orchestrating economic development and decarbonization goals within resource endowments.

This study investigated the impact of renewable energy indicators on sustainable development in Nigeria over the period 1990 to 2024. The main objective was to examine how solar energy consumption, total biomass consumption, and hydroelectric production influenced sustainable development as measured by the Human Development Index. The motivation for the study was rooted in Nigeria’s persistent energy challenges, its growing reliance on renewable sources, and the urgent need to understand how these transitions translate into human development outcomes. The methodology combined descriptive statistics, unit root tests, bounds testing for cointegration, and the Auto-Regressive Distributed Lag model to analyze both short-run and long-run dynamics. The statistical techniques ensured that the relationships were rigorously examined despite the mix of integration orders among the variables. The findings revealed three distinct outcomes. First, total biomass consumption had a positive but statistically insignificant effect on human development, implying that its dominant traditional use limited its developmental contribution. Second, solar energy consumption had a negative and significant effect, suggesting that despite its rapid growth, structural and institutional challenges such as high costs, weak policy frameworks, and poor integration constrained its ability to improve welfare outcomes. Third, hydroelectric production had a positive and significant effect, confirming its role as the most stable and impactful renewable source for long-term improvements in education, health, and income levels. The error correction mechanism further confirmed a strong speed of adjustment, indicating that the system quickly returned to equilibrium after short-term shocks. The study recommended that the Federal Ministry of Environment and the Energy Commission of Nigeria should lead efforts to modernize biomass through clean technologies. The Rural Electrification Agency and the Nigerian Electricity Regulatory Commission should strengthen financing and grid integration to enhance solar’s developmental impact, while the Federal Ministry of Power and the Transmission Company of Nigeria should prioritize the expansion and climate resilience of hydroelectric infrastructure. Collectively, these actions were necessary to maximize the developmental benefits of renewable energy in Nigeria.

Introduction The accelerated development of renewable energy sources has confronted substantial challenges, primarily attributable to their intermittency and uncertainty. Consequently, the integration of green electricity has become a pressing concern. Hydrogen production from water electrolyzer has emerged as a key method for promoting local wind and solar energy consumption. However, extant studies tend to neglect the value of hydrogen as a chemical feedstock and rely on simplified linear models to describe the characteristics of electro-hydrogen coupling devices. This has resulted in discrepancies between optimization decisions and actual operational performance. Methods To address this gap, the present paper employs a nonlinear semi-empirical model with a focus on electrolyzer and fuel cell. It describes the energy conversion between electricity and hydrogen more accurately based on electrochemical mechanisms. On this basis, considering the dual value of hydrogen energy as both “energy carrier” and “chemical raw material”, the operation optimization model of electric-hydrogen coupling system for chemical parks is established. Furthermore, a convexification method for coupling device constraints is proposed to enhance solution efficiency. Results and Discussion The findings of the study demonstrate that the semi-empirical model provides a more accurate representation of actual equipment performance, thereby preventing deviations between real-world operation and outcomes derived from optimization. Furthermore, the collaborative optimization strategy that accounts for hydrogen’s dual value has been shown to significantly improve the system’s economic benefits.

Solar energy has become the core driver of global energy transformation. To achieve a more accurate prediction of the global solar energy consumption, this study presents a novel conformable fractional incomplete gamma grey model (denoted as CFIGGM). In this new model, the conformable fractional-order accumulation operator is introduced to fully mine the information of small samples and reduce the dependence on data distribution. Meanwhile, the Whale Optimization Algorithm is also utilized to solve the optimal value of nonlinear parameters in the newly proposed model to enhance its prediction performance. Moreover, numerical experiments are carried out on five sequences to verify the performance of the new model. The experiments’ results show that the proposed model has better prediction performance than the comparative models. Then, the new model is applied to forecast the global solar energy consumption. The fitting MAPE of the newly proposed model is 0.07% on the training set, and the prediction MAPE is 0.78% on the test set. As an application, the trend in global solar energy consumption is predicted by using the proposed model. Its result shows that the global solar energy consumption is projected to maintain a strong growth momentum, but the growth rate will slow down in the future. The results can serve as strong supporting data for relevant departments and enterprises.

• Electric, solar energy, and fuel consumption in coastal low-cost dwellings of rural and semi-urban settings were quantified in Tropical eastern India. • Electric and solar energy consumption significantly differed among household communities and across seasons. • Energy consumption is influenced by indoor environmental factors and residents’ behavioural perception. • Interactions between building materials and overall heat transfer coefficient (U) in rural and semi-urban settings was quantified. • Bioclimatic design strategies can improve the energy efficiency of the coastal low-cost dwellings and help promote eco-energy-human friendliness. The built environment accounts for nearly one-third of global energy consumption and CO₂ emissions. While urban areas lead in energy demand, rural and semi-urban regions in developing countries are seeing increasing energy needs due to population growth, changing lifestyles, and expanding electrification. In tropical coastal regions, improving energy efficiency in low-cost homes presents a unique challenge due to the unpredictable climate and inconsistent use of bioclimatic design principles. Despite the importance of this issue, limited research has been conducted to quantify energy consumption patterns in these areas and develop tailored design strategies that address specific socio-environmental conditions. This study seeks to fill that gap by analyzing energy usage patterns and efficiency in rural and semi-urban dwellings in tropical coastal regions while suggesting context-specific bioclimatic design solutions. A thorough field study was conducted across three different categories: remote rural, rural, and semi-urban areas in the eastern coastal region of India, covering a sample size of 1332 dwellings. Data were collected through a structured questionnaire survey to capture seasonal variations in energy consumption, indoor environmental quality, and housing characteristics. Statistical analysis was used to assess how factors such as dwelling components, local materials, and behavioral patterns influenced energy consumption. The results revealed significant differences ( p < 0.05) in energy consumption patterns across different seasons and communities, with significant variations in the use of electricity and solar energy. Locally available fuels emerged as the dominant energy source, reflecting a strong dependence on traditional energy practices. Additionally, the thermal transfer coefficients of walls, doors, windows, and roofs ranged from 0.94 to 5.9 W/m².K, indicating considerable variation in thermal conductivity depending on the materials used. These findings highlight the importance of incorporating effective bioclimatic design strategies that can enhance heat resistance and improve indoor thermal comfort while minimizing energy consumption. Implementing these strategies may significantly lower energy usage and enhance thermal comfort in low-cost coastal and rural homes, contributing to long-term sustainability and climate resilience. The study can assist policymakers, architects, and planners with actionable recommendations for developing energy-efficient, climate-responsive dwellings to improve the well-being of communities in Tropical coastal regions.

With the growing electricity demand and tariffs, residential consum ersare opting for rooftop solar systems to susta in their electricity needs. While the houses have a ccess to solar energy during the day, but they still relyheavily on the grid during night, adding stress to national grid and intensifying grid’s energy loss. This paper presents a Dem and Side Management (DSM) strategy designed to optimise solar energy usage in grid connected Photovoltaic (PV) systems within Pakistan residential sector. By implementing DSM strategy, this study encourages consumers to shift their high energy appliances to coincide with peak solar production hours, thus reducing the reliance on the grid during non-solar hours. Solar energy production was assessed using PVsyst software, while energy consumption patterns of households were estimated through data acquisition system and LabVIEW. The DSM technique resulted in more efficient solar energy utilisation, decreased peak demand on the electric system, and less load shedding. This solution promotes the provision of clean energy under the United Nations (UNs) Sustainable Development Goals (SDGs) while also a ddressing Pakistan's energy concerns. Finally, the findings support the use of DSM to increase the efficiency of residential PV systems, improve grid stability, and promote green energy culture in Pakistani homes.

The Group of Twenty (G-20) nations face a critical challenge in balancing economic growth, industrial development, and rising carbon emissions while advancing clean energy transitions. This study assesses how tourism, trade, industrialization, economic growth, and CO 2 emissions influence solar energy consumption in G-20 economies from 2000 to 2021. Using the method of moments quantile regression to capture heterogeneous sectoral impacts, the research reveals that tourism and carbon emissions positively and significantly influence solar energy adoption, reinforcing its role in sustainable development. However, it is negatively associated with trade and industrial output, pointing to structural or policy barriers within traditional sectors. The results vary across quantiles, highlighting that the influence of solar energy depends on the level of economic development and adoption capacity. These findings offer timely insights for policymakers aiming to design targeted, sector-specific renewable energy strategies that balance growth with environmental responsibility. This study aligns with Sustainable Development Goals 7 and 9, as outlined in the United Nations’ 2030 Agenda for Sustainable Development, by highlighting the role of clean energy and sustainable industry in transforming G-20 economies.

The global energy market is significant in establishing economic activities and policy decisions, and hence, Accurate forecasting of renewable energy consumption is critical for optimizing grid management and achieving decarbonization targets. This study develops a seasonal ARIMA (SARIMA) model to predict solar and hydro energy consumption across 140 countries (1965–2023), leveraging hyperparameter tuning (p, d, q) = (2, 1, 2) for solar and (1, 0, 1) for hydro energy. RMSE and R² scores are used to model evaluation metrics for validity of the predictions, our model achieves high accuracy (RMSE: 3.45 solar, 4.12 hydro; R²: 0.87 solar, 0.81 hydro) and reveals divergent trends: hydro consumption remains stable, while solar adoption fluctuates with policy and market shifts. The results provide actionable insights for energy planners to mitigate intermittency challenges and align infrastructure investments with regional renewable potential.

Poor access to clean energy impacts the livelihood of the Nigeria rural population. This study investigated the use of solar energy in tackling the recurrent energy access challenges in Isoko South, Nigeria. Both qualitative and quantitative methods were applied to determine the status of access to electricity and the economic competitiveness of solar energy solutions. Questionnaires were administered to 250 randomly selected rural households. Results suggested that 76.8% of the respondents use grid-connected electricity, out of which, 49% operate back-up generators in the event of grid power failure. Notwithstanding, 7.6% of the respondents do not have access to any form of electricity. All the respondents demonstrated the willingness to pay for reliable and cleaner energy solutions. To fully electrify a one-bedroom apartment with 17.9 kWh of solar energy, daily, a life cycle cost of 2.64 million naira was estimated. Solar energy consumption of the one-bedroom apartment is at a unit cost of energy of 57.57 naira, with a payback period of 5 years. Due to the high upfront cost of solar solutions, it is recommended that the government should provide a solar subsidy (like the defunct petrol-based subsidy) and fiscal policies that promote solar energy penetration in the rural areas.

Abstract - Mankind is living in the arena of evolution of technology in which energy is an integral part. However, this enrichment of science and technology has opened new pathways for energy consumption and generation as well. Raise of population density is another root cause for energy demands. Fossil fuels are depleting at faster rate and leaving carbon footprints on environment. In this scenario, it is inevitable to depends on renewable energy sources which are inexhaustible, eco-friendly and abundantly available in nature. Amid all renewable sources, effective utilization of solar energy is a viable alternative to meet the growing energy demands, particularly for low temperature applications. While consumption of solar energy for various domestic applications is not new, but it is suffering from lower effective energy conversion problem.Solar collectors are the device that absorbs the incident solar radiation and converts into useful form. Among different types of collectors, solar flat plate collectors (SFPC) are the noteworthy devices to convert the incident radiation into heat energy of working fluid. In view of enhancing the thermal efficiency of collectors, there are two major approaches as either changing the geometry and operating parameters or enhancing the properties of working fluid. Since, various geometrical and operating parameters and modifications are more or less saturated, present work is focused on enhancing the thermophysical and optical properties of working fluid by suspending nanoparticle as an approach for effective conversion of solar radiation into useful heat energy.Thermophysical properties and their influence on thermal performance of SFPC are estimated using the empirical correlations available in open literature and comparisons are made with the experimental outcomes. It is noticed that a substantial variation exists between the analytical and experimental outcomes. Thus, the influence of each parameter on collector efficiency is critically examined. Both thermal conductivity and viscosity of all working fluids are experimentally measured and compared with the existing correlations. Key Words: Solar flat plate collector, solar device, solar air heaters, solar energy.

The increasing demand for renewable energy has driven the development of intelligent solar energy systems. This project presents an IoT-Based Smart Solar Energy Monitoring System that enhances the efficiency, reliability, and real-time management of solar power generation. The system integrates Internet of Things (IoT) technology with solar energy systems to provide continuous monitoring and optimization. The proposed system consists of solar panels, sensors, microcontrollers, and a cloud-based platform. Sensors measure critical parameters such as solar irradiance, temperature, voltage, and current, while a microcontroller processes this data and transmits it to a cloud-based server via Wi-Fi or GSM modules. The collected data is analyzed using algorithms to predict energy generation patterns, detect faults, and improve energy utilization. Users can access real-time data and analytics through a web dashboard or mobile application, allowing remote monitoring and control. The system also includes automated alerts for maintenance and performance anomalies, reducing downtime and improving efficiency.By providing an efficient, cost-effective, and scalable solution, this IoT-based smart solar energy monitoring system contributes to the advancement of sustainable energy. It empowers users with real-time insights, optimizing solar energy consumption and promoting environmental sustainability.

This paper examines the performance of photovoltaic panels integrated into the electrical grid in a load-sharing scheme at Darool Ehsan Muhammadiyah Senior High School, Sragen. The study is part of Universitas Muhammadiyah Surakarta's community service program focused on renewable energy development in educational environments. Unlike previous studies relying on simulated data, this research uses real-time primary data from direct measurements of photovoltaic power production and grid electricity consumption over a specific period. The study’s innovation lies in analyzing the photovoltaic system within a tropical climate-based load-sharing scheme and comparing energy usage when connected to photovoltaic panels versus when disconnected. It also evaluates the impact of reducing solar energy consumption from the grid. The collected data reveal an estimated daily load of 49.89 kWh, with PLN-supplied energy consistently exceeding inverter-supplied energy. Integration of photovoltaic panels into the grid reduces PLN electricity consumption by up to 30% during optimal sunlight periods, achieving an average system efficiency of 15%. This research offers valuable insights into the potential and challenges of implementing photovoltaic panels in educational settings, emphasizing the importance of climate considerations in system design and optimization. Future studies will focus on techno-economic evaluations of solar power installations and harmonic distortion (THD) analysis for larger-scale implementations.

A new era of solar energy consumption has been triggered by technological developments combined with the growing need for renewable energy sources. Solar-based photovoltaic (PV) systems have become a viable option for producing clean electricity. However, a number of variables including temperature, irradiance and shading conditions have a significant impact on the efficiency of solar panels. In order to maximize the energy output of solar panels, sophisticated control mechanisms are needed that continuously track the point of maximum power output under a variety of environmental circumstances. For this integrating next-generation computing platforms such as Raspberry Pi, offers a powerful opportunity to improve the effectiveness and efficiency of solar PV systems. Raspberry Pi is a perfect platform for creating complex monitoring and control systems for renewable energy applications, thanks to its small size, low cost and powerful computing power. This paper presents the step-by-step development of a real-time monitoring and control system for solar photovoltaic system using a Raspberry Pi. The work can be used as a tutorial by students in physics lab classes, who will be able to integrate knowledge of renewable energy, electricity, efficiency and information technology.

Objectives: As conventional energy dwindles and environmental issues rise, solar energy gains traction for its sustainability. Rooftop potential, improved panels, affordability, and easy installation drive its urban adoption, necessitating accurate PV panel feasibility assessments. Methods: This study aimed at measuring rooftop solar PV potential and energy consumption and production using a GIS based tool within ArcGIS City Engine. Analysing the built footprint data enables the estimation of PV potential. Findings: Studying the Gandhinagar Smart City revealed the existence of 39,738 unique rooftops covering a total area of 56,976,756.77 square meters. Additionally, it was revealed that their annual PV potential was found to be 1,017.74 MWh, while the replacement potential dwarfs to 1,216.75 MWh every year. However, in terms of environmental benefits, it is estimated that a shift towards solar based energy systems in urban environments can mitigate AGCC effects by reducing annual CO2 emissions up to 10.40 million tons. Novelty: This study introduces a novel GIS-based approach within ArcGIS City Engine to accurately assess rooftop solar PV potential at an urban scale. The analysis of Gandhinagar Smart City provides a comprehensive evaluation of rooftop energy potential, distinguishing between generation capacity and replacement potential. Keywords: 3D City Modelling, CO2 Emission Reduction, Decentralized Energy Systems, GIS-Based Analysis, Rooftop Solar Potential

In order to reduce energy waste caused by insufficient absorption capacity, improve the stability and reliability of the wind and solar energy storage system, reduce power costs, reduce greenhouse gas emissions, and enhance environmental benefits. A new optimization method for vanadium redox batteries that considers the wind and solar absorptive capacity is studied. The outputs of the wind worm wheel, photovoltaic, and new vanadium redox batteries in the wind–solar vanadium redox battery are calculated and analyzed. Based on the calculation results, the wind light memory capacity majorization arrange blueprint is constructed, and the impersonal ability of the blueprint is to determine the maximum total consumption of recyclable energy, the highest energy utilization efficiency, the minimum cycle energy of energy memory battery and the minimum cycle fever memory of fever memory tank, and the minimum amount of wind and light discarded. After the relevant constraints are designed, the upgraded quantum particle flock majorization arithmetic is used to solve the impersonal ability to obtain the arrange majorization results of the new vanadium redox battery. The test results show that after the majorization of energy memory arrange by this method, the maximum values of distributed generation absorption ratio, load peak valley difference degree, and load fluctuation degree are 0.74, 0.15, and 0.17, respectively; energy utilization efficiency reaches 75.5%; the change of the state of condition of the vanadium redox battery is relatively gentle, and the fluctuation range is between 0% and 60%; the effective wind and solar absorptive capacity of each node is above 11 GW/h.

Japan’s local consumption of solar energy: The role of energy demand in residential and small-scale solar projects

The growing global demand for sustainable energy highlights the need for further advancements in solar energy, especially in the world’s largest economies. This study examines the impact of solar energy consumption (SEC) on tourism growth in European countries, specifically Austria, Belgium, Bulgaria, the Czech Republic, France, Germany, Greece, Italy, the Netherlands, and Spain, which exhibit significant SEC and substantial tourism inflows, from 2000 to 2021. Using a comprehensive panel data analysis, the study employs the method of moments quantile regression (MMQRG) approach to evaluate this relationship across different quantiles. The findings reveal a significant positive effect, indicating that a 1% augmentation in solar utilization results in a 0.37% increase in tourism. Additionally, SEC positively influences the information and communication technology (ICT) sector, with a 1% increase in solar energy use leading to a 1.097% rise in ICT development. Furthermore, greater solar energy adoption reduces carbon dioxide (CO 2 ) emissions, reinforcing its environmental benefits. The results underscore the importance of solar energy investment in fostering sustainable tourism and economic development. This study provides valuable insights for governments, businesses, and stakeholders seeking to leverage solar energy to drive tourism expansion and environmental sustainability.

Abstract This research introduces an advanced adaptive control framework utilizing deep reinforcement learning, specifically the Asynchronous Advantage Actor‐Critic algorithm, to optimize the operation of photovoltaic‐enriched microgrids integrated with solar electric vehicles. The integration of solar electric vehicles within microgrids not only addresses transportation needs and energy sustainability by acting as dynamic energy storage systems. The inherent intermittency of solar power and the dynamic energy demands of solar electric vehicles pose significant operational challenges, requiring robust, flexible control systems capable of real‐time optimization. Our framework leverages the Asynchronous Advantage Actor‐Critic algorithm for its efficiency in handling high‐dimensional state spaces and its capability for rapid, concurrent learning processes, making it well‐suited for the dynamic and complex environment of photovoltaic‐enriched microgrids. The proposed model innovatively combines solar energy generation with solar electric vehicle energy storage and consumption dynamics, providing a holistic approach to microgrid management that optimizes energy flows, reduces reliance on traditional energy sources, and minimizes environmental impact.

This research aligns with the Sustainable Development Goals (SDGs), particularly SDG 7 (affordable and clean energy) and SDG 13 (climate action), by introducing a comprehensive model for carbon emissions reduction through the substitution of clean energy to achieve the SDGs. The analysis begins with panel regression using historical data from 2008 to 2023, focusing on selected countries in the Asia-Pacific region, including coal-exporting countries like Indonesia and Australia, as well as coal-importing countries like China. The analysis is then extended with an ARIMA approach to forecast coal and solar energy consumption for the period 2024-2030. Subsequently, the forecast for carbon emissions reduction is conducted on average, analyzed using the previous panel regression equation by introducing values from the ARIMA analysis into pessimistic, moderate, and optimistic categories. The findings of this study indicate that solar energy consumption plays a crucial role in reducing carbon emissions, while coal consumption increases carbon emissions. On the other hand, the optimistic approach can facilitate the dominance of solar energy in the overall energy mix, resulting in significantly lower carbon emissions growth rates. Therefore, this research emphasizes the need to implement an optimistic model to drive the achievement of SDG 7 and SDG 13 by 2030.