This study presents the design and implementation of a solar power generation system (SPGS) to harness solar energy as an alternative power source for greenhouse operations. The system is developed to support vertical hydroponic crop cultivation while operating independently through an off-grid configuration. The specific objectives of this research are to optimize the energy efficiency of the SPGS, ensure the reliability of power supply for hydroponic operations, and evaluate the system's effectiveness in supporting sustainable agricultural practices. The SPGS utilizes solar panels to convert solar radiation into direct current (DC), which is stored in batteries or converted to alternating current (AC) to power various loads. The results showed that the SPGS operated effectively and was capable of supplying consistent energy to the greenhouse. The highest recorded solar irradiance was 1072.82 W/m², resulting in a voltage of 42.8 V and current of 6.9 A. The maximum power output reached 295.32 W, with the solar panel system achieving an efficiency of 18.72%. The combination of a solar energy system specifically created and fine-tuned for greenhouse use, along with a vertical hydroponic system. This research offers a customized energy approach that guarantees effective energy collection, storage, and delivery, perfectly aligned with the fluctuating energy needs of a greenhouse setting.

Many studies have suggested various techniques to replace fossil fuel, including diesel, whose combustion emits exhaust pollutants which in turn lead to a risk to the environment and public health. The combustion of water-diesel emulsions can emit fewer exhaust pollutants when fueled by a diesel engine. In Iraq, Iraqi crude oil is characterized by a high percentage of sulfur in it, which makes all its derivatives contain sulfur percentages. Iraqi diesel sulfur content ranges from 1% to 2.5%. Sulfur causes an increase in some dangerous pollutants, such as sulfur oxides. If these oxides combine with the emissions of nitrogen oxides (NOX) and unburned hydrocarbons in the atmosphere, they will cause smog clouds with significant health and environmental risks. In this experimental study, the performance of a diesel engine was investigated at constant load and variable speed of the engine, using three types of emulsions (water-diesel). Brake-specific fuel consumption (BSFC) is reduced when working with 10% and 20% water emulsions. In contrast, 30% of water emulsion consumption was increased. The NOX emissions declined by 14%, 16%, and 29% for emulsions 10%, 20%, and 30% water added to diesel, respectively. As for hydrocarbons, they decreased by 1% and 9.3% in the case of adding 10% and 20%. Hydrocarbon levels increased by about 33.6% when 30% water was added to diesel. Experiments have shown that both NOX and PM decrease together when using 10% and 20% emulsions.

The smart-grid-enabled demand-side energy management is used to regulate consumer energy demand. The consumer can adjust their energy consumption in response to the pricing strategy of the grid in the market-based programs. The energy bidding methodology is proposed to predict the electricity rate and optimize the energy demand of the chiller system for energy consumption and cost minimization. The forecasted electricity price and the energy demand schedule generated by the optimization algorithm are used to bid in the electricity market. To forecast the electricity rate, a hybrid model Hilbert Transform-Based Long Short-Term Memory (Hilbert-LSTM) is proposed and the results indicate the improvement in the prediction accuracy in terms of the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The energy consumption is optimized in the dynamic electricity tariff to generate an optimal energy demand schedule. The bid electricity price is calculated for three different cases and the bidding cost and bidding reliability for the optimized energy demand schedule are compared. The results show that the bidding cost is reduced by 37% and bidding reliability is the highest for the proposed electricity forecasting model Hilbert-LSTM.

The study uses embedded machine learning (ML) to focus on solar energy harvesting and storage optimisation. The research investigated environmental parameters, input features, including temperature, relative humidity, target variable, month and day, and solar surface radiation. A dataset for 5 years was used. An ML algorithm was employed for the study, and the linear regression feedforward neural network (FFNN) was used. The normal root mean squared (nRMSE) and R-squared (R2) scores were used as criteria to evaluate the model's performance. A solar tracker system, built with Arduino and ESP32 microcontrollers, maximises energy collection. The system harnesses the power of solar panels to convert sun radiation into electrical energy, which is then stored in a 3.7 V rechargeable battery. This battery powers the sensors, ensuring continuous operation. The root mean squared error (RMSE) value was 80.48 W/m², which measures the typical prediction error and optimises energy harvesting. The R2 of 0.896 shows the model experiences ~90% of the solar irradiance variability data. The higher R2 ensures the model reliably captures environmental parameters critical for adjusting solar panels and maximising energy efficiency. The research's practical implications show that we can have a high uptime for solar power systems, close to 24 hours. Embedded ML can enhance renewable energy management.

Biomass, as a separate type of granulated solid fuel, ranks third in terms of the share of generated electricity and in a number of countries is the main type of fuel in the production of thermal energy. Made for home heating systems, but might work in commercial and industrial settings as well. Fuels such as sawdust, wood chips, and wood mill waste, as well as recycled wood from disassembled pallets or furniture, often have a high energy density (16-19 MJ/kg), and their ash level varies depending on the kind of fuel. However, burning these fuels poses environmental challenges such as air pollution and greenhouse gas emissions. This work focuses on the state of production of granular solid fuels, including their types and potential applications. To understand the underlying phenomena and chemistry of combustion, as well as to design and run different combustion devices to enhance the conversion efficiency of these fuels into energy. The main study area centered on a granulation process, whereas fines are agglomerated into larger granules for better handling and combustion characteristics. It evaluates the current technology approaches employed in producing and utilizing these fuels as a granulator for domestic waste. The evidence also points to the importance of understanding the combustion processes desired for optimization, lessening environmental impacts, and the importance of pyrolytic processes in transforming solid particles that determine total combustion efficiency.

Indonesia’s tropical climate and hilly terrain offer significant potential for renewable energy, particularly solar and wind. This study presents a hybrid power plant prototype integrating solar and wind energy, equipped with an Arduino Mega-based Auto Switching system and a web-based monitoring application. The system optimizes energy use by prioritizing solar energy during the day and wind energy at night. Key components include GY-49 lux sensors, anemometers, voltage sensors, charger controllers, batteries, and inverters. Experimental results show effective source switching, with solar energy achieving a maximum voltage of 21.65 V at 65,058.8 lux and wind energy producing 0.79 V at 16 m/s. However, wind energy output is insufficient for direct battery charging, indicating a need for turbine design optimization. The web-based monitoring system ensures real-time performance tracking, enhancing system reliability. This prototype offers a sustainable energy solution for hilly regions with limited grid access, reducing reliance on fossil fuels.

The increasing worldwide energy requirements, combined with sustainable urban growth, drive the need for inventive building technologies. Building integrated photovoltaic and thermal systems (BIPV/T) generate both electricity and thermal energy while enabling nearly zero-energy buildings (NZEBs) to achieve their energy goals. Our analysis examines the technological, economic, and environmental aspects of BIPV/T systems and their application within building elements such as roofs, facades, and glazing areas. The review also examines supportive policy frameworks for BIPV/T implementation while pinpointing adoption barriers like steep initial investments and incomplete regional policies. The present review is based on a systematic literature review from January 2010 to March 2025 from the Scopus, Web of Science, and IEEE Xplore databases. A search string consisting of the combination of the keywords building integrated photovoltaic and thermal systems (BIPV/T), nearly zero-energy buildings (NZEBs), solar technologies, Aesthetic and Architectural Integration, Energy Efficiency, and Building codes. A total of 75 articles were selected after screening and eligibility assessment. This study seeks to provide guidance for researchers, architects, and policymakers to progress BIPV/T integration towards sustainable urban development.

This paper proposes a Hybrid AC/DC Microgrid (HMG) system comprising a wind energy control system (WECS), a photovoltaic (PV) system, and a battery system. Because microgrids emit fewer carbon gases and may be connected to the utility grid, researchers are finding them more and more appealing. The HMG increases system efficiency and power quality by reducing multiple reverse conversions. The VSC system functions as a DC/AC bus control system and employs used bacteria foraging optimization (BFO) algorithm to adjust the proportional integral (PI) controller settings to reduce AC and DC switching. Furthermore, for the battery energy storage system (BESS) and wind turbine speed regulation utilize two PI controllers. Lastly, the maximum power point (MPP) of the PV system was investigated using perturbation and observation (P&O), incremental conductance (IC), fuzzy logic controller (FLC), and maximum power point tracking (MPPT). The FLC technique showed the benefit of attaining the finest outcomes, specifically power (99.68 kW) and efficiency (99.84%). The results show that the suggested approach works well for accomplishing the primary goals of the hybrid microgrid. The simulations in this study were carried out using MATLAB/Simulink.

Bioethanol can only continue as a viable cleaner alternative to fossil fuels by utilizing abundant, renewable, and eco-friendly feedstocks with high conversion efficiencies or by developing technologies that enhance efficiency and reduce inhibition. This study aims to compare the potential of producing bioethanol from Artocarpus heterophyllus Lam. (AHL) pulp starch with cassava (CAS) starch. Response surface methodology (RSM) was used to optimize the process conditions in acid and enzymatic hydrolysis for optimum reducing sugar and ethanol yield. The study demonstrated that AHL performed better than CAS in the enzymatic process with an optimum reducing sugar yield of 80.22 g/L compared to 70.61 g/L obtained for CAS. The conversion efficiencies for AHL and CAS at an optimum condition of 120 amylase and 310 amyloglucosidase unitg-1 starch were 91.2% and 80.24%, respectively. Consequently, in the acidic process, an optimum sugar yield was achieved at 0.5 M H2SO4, 45 mins hydrolysis time and 121℃. Under these conditions, AHL sugar yield was 19.05 g/L with 34.64% conversion efficiency while CAS produced 22.48 g/L with 40.87% conversion. The results of the ethanol yield obtained in both hydrolytic processes showed that AHL compared very favorably with CAS. Though AHL is characterized by higher amylose content (28.90) than CAS (20.43) which would easily hinder enzyme accessibility during hydrolysis, its type-A crystal structure paved the way for its starch to be easily assessed by the α-enzymes. Hence, this study provided a suitable, efficient and sustainable substitute to cassava or other first-generation feedstock for bioethanol production.

Solar energy for power generation has increased significantly due to population growth and economic expansion. Solar irradiance is a primary determinant of solar photovoltaic (PV) technology. However, high-quality ground-based solar irradiance measurements remain scarce. Accurate prediction of global solar irradiance has become essential for grid distribution, financial planning, performance assurance, operational efficiency, and safety in solar PV systems. In this study, various Machine Learning (ML) algorithms—including Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbors (KNN), Decision Tree (DT), Multilayer Perceptron (MLP), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Linear Regression (LR), were employed to predict solar irradiance in Jerusalem, Palestine. Data was collected from a meteorological station in Jerusalem over a one-year period, from January 1, 2023, to December 31, 2023. Eight critical features influencing solar irradiance prediction were collected and analyzed, including diffuse irradiance, direct irradiance, mean temperature, pressure, relative humidity, wind speed, and wind direction. The models' accuracy was assessed using the coefficient of determination (R2), Root Mean Square Error (RMSE), relative Root Mean Square Error (rRMSE), and Mean Absolute Error (MAE). The results indicate that the RF model achieved the highest accuracy in predicting solar irradiance, with metrics of R2=0.90, RMSE=104.58 W/m2, rRMSE=0.24, and MAE=63.29 W/m2.

The urgent need for a more responsive built environment in the era of climate change has driven architects, researchers, and construction professionals to adopt integrated design solutions that utilize various technologies to improve buildings' energy performance. This effort aims to reduce dependence on fossil fuels, whose combustion significantly contributes to climate change. Notably, the building sector consumes approximately 41% of electrical energy, with most of this energy used for ventilation, cooling in summer, heating in winter, and artificial lighting. This comparative study employs a descriptive methodology, gathering information about energy sources, their types, and their impact on the construction sector. Additionally, it analyses architectural projects that have adopted smart envelopes as a remedial measure to combat climate change. The research then explores modern treatments for contemporary building envelopes and their transformation into smart envelopes by elucidating the concept of intelligence within these systems. Experiments implementing these methods in the Middle East and North Africa (MENA) region are reviewed, highlighting the benefits and lessons learned. The study emphasizes the impact of renewable energies and their integration with the building envelope, as well as negative treatments at the envelope level that contribute to isolating the building from external environmental conditions. The findings provide a comprehensive description of how different variables affect the energy performance of buildings.

Promoting individual energy-saving behavior is crucial to addressing environmental challenges and advancing sustainable development. While this topic has been explored in the existing literature, research specifically examining energy-saving behavior in workplaces (ESBW) through a bibliometric analysis remains scarce. This study aims to fill this gap by conducting a bibliometric approach using performance analysis and science mapping techniques to evaluate and synthesize research on ESBW. A comprehensive search strategy was employed to extract relevant documents published in the Scopus database. The collected data were analyzed using VOSviewer and RStudio software. A total of 194 documents were identified as scientifically published in this field from 2005 to 2024. The analysis highlights the most prolific sources, authors, countries, and articles. Furthermore, based on keyword co-occurrence analysis, this study found the main thematic clusters of ESBW, including energy-saving related to behavioral theories, motivational and social factors promoting sustainability, energy conservation in the workplace, and occupant behavior connected with energy efficiency. The theoretical foundation can benefit future research in developing effective policies and strategies to encourage energy-saving practices in workplace environments.

Renewable energy installations are rising at a fast pace because societies r./uire both emission reduction and alternative clean energy sources. Policymakers, together with industry stakeholders, find it troublesome to use traditional energy prediction models because these systems operate without clarity and fail to handle intricate market systems properly. This research solves these issues through a machine learning (ML) model prediction of renewable energy use. Then, it enhances predictions through explainable artificial intelligence (XAI) methods to achieve better accuracy and trustworthiness. Our analysis includes multiple ML algorithms from the ensemble category consisting of Random Forests (RF) and Gradient Boosting in addition to advanced boosting algorithms XGBoost and Light Gradient Boosting Machines (GBM). Local Interpretable Model-Agnostic Explanations (LIME) reveal the decision-making procedures during predictions while delivering understandable explanations about the model's conduct to users. The methodology adopts a thorough model testing methodology using extensive datasets, which include multiple variables related to renewable energy consumption, including economic metrics and environmental aspects. Researchers obtained predictive performance excellence with interpretability benefits from their models in predicting renewable energy usage. The Light GBM model delivered 97.40% accuracy when analyzing data, while the LIME process showed GDP growth and electricity access as key determining variables. XAI integration in renewable energy forecasting presents important progress that livers enhanced, transparent yet actionable energy predictions that build trusted reliability for use in the industry. The study demonstrates the power of uniting ML with XAI techniques for better comprehension of renewable energy patterns, which enables better decisions for sustainable energy development.

This research examines the impact of twisted tapes (TT), both with and without triangular-winglet obstructions positioned at different attack angles (30°, 45°, and 60°), on thermal and flow dynamics within a circular tube under steady heat flux conditions. The TT is 22.5 mm wide (w) and 100 mm long (y). The investigation is undertaken for Reynolds numbers (Re) ranging from 5000 to 25000. We looked at four winglet height ratios (HR): 0.18, 0.145, 0.1, and 0.07. The results show that the winglet attack angle of 60° gives the biggest boost to the Nusselt number (Nu) compared to the other setups, with a clear improvement. The 60° angle caused the Nu to go up by 13% compared to the 30° configuration and by 21% compared to the 45° configuration. Also, at Re = 5,000, the 60° winglet angle had the best overall thermal performance factor (η = 1.80), followed by the 45° (η = 1.76) and 30° (η = 1.69) arrangements. The tube with simply TT and no winglets, on the other hand, had the worst overall performance, with η = 1.53.

Soiling, defined as the accumulation of dirt, dust, and other particles on the surface of photovoltaic (PV) panels, is a significant issue that substantially impacts solar panel efficiency and performance. This accumulation leads to energy losses and decreased electricity output. Numerous research papers have proposed various systems to address this issue. This paper provides a comprehensive review of recent publications on soiling detection in solar panels. The review methodology includes literature retrieval, screening, content analysis, and bibliometric analysis, utilizing the Scopus database to compile a final selection of 75 papers. This review identifies gaps in previous research, such as the need for more robust and cost-effective detection systems and the integration of emerging technologies like artificial intelligence and remote sensing. Key findings highlight that deep learning models and advanced sensor technologies show promising results in improving soiling detection accuracy. The review also suggests potential areas for future work, emphasizing the development of innovative inspection tools, models, and cleaning systems that can enhance efficiency and reduce operational costs.