Air-cooled chillers remain important to climate control in many industries. However, ensuring high availability of such systems has remained a big challenge to their operations, especially in developing countries like Tanzania. The maintenance practices currently adopted are mainly reactive, translating to a frequent occurrence of failures that result in shortened equipment life and energy wastage. In this paper, an optimum data-driven model is developed and validated for managing the maintenance of air-cooled chillers to advance the performance of their availability. Quantitative and qualitative data were collected from seven chillers at Julius Nyerere International Airport using a mixed-methods methodology approach at Dar es Salaam, including operations records, performance indicators, and interviews with experts. Multiple regression analysis highlighted preventive maintenance frequency (β = 0.312), compressor performance (β = 0.241), control system reliability (β = 0.225), and energy performance (β = 0.202) as the strongest positive determinants of availability. Contrary to findings in other studies performed under more extreme climates, environmental factors such as ambient temperature and dust had low significance. This can be explained by the stable coastal climate of Dar es Salaam and the relatively controlled infrastructure at JNIA. The model was then validated with an average prediction accuracy of about 98%. This was indicative of its efficiency in predicting the availability of chillers.

Abstract Climate change has become a critical driver of energy policy and building performance assessment worldwide. In response to rising greenhouse gas emissions and increasing energy demand, the European Union introduced the Energy Performance Certificate (EPC) system in 2002 to standardize and promote energy efficiency in buildings. However, conventional EPC assessments are still based on static weather inputs such as Typical Meteorological Year (TMY) data, which overlook the growing influence of climate variability and long-term warming trends. This study proposes a dynamic simulation framework that incorporates both global warming and interannual climate fluctuations into building energy evaluations. Using 45 years of ERA5 reanalysis data (1979–2023) and EnergyPlus simulations of a standardized residential building in Korea, we quantify how external climate dynamics affect heating and cooling demand. Results reveal that short-term variability often exceeds the energy impacts of four decades of warming, with spatial differences influenced by topography and ENSO phase. These findings highlight the limitations of current EPC methodologies and underscore the need to integrate both gradual and dynamic climate influences into future EPC frameworks to ensure more resilient energy planning.

The rapid growth of renewable energy adoption has heightened the need for accurate solar energy prediction to ensure grid stability, particularly in regions with high solar penetration. However, traditional forecasting methods relying on historical meteorological data often fail to address short term fluctuations caused by dynamic cloud movements, limiting real time adaptability. To overcome this challenge, this study proposes a deep learning framework integrating convolutional neural networks (CNNs) with attention mechanisms to predict photovoltaic (PV) output from radiance sky images. Two datasets capturing diverse sky conditions were used to evaluate three architectures: a baseline CNN, CNN with Squeeze-and-Excitation (SE) Attention, and CNN with Spatial Attention. The CNN with SE-Attention model significantly outperformed baseline models, reducing prediction errors and improving explanatory power, as validated by metrics including RMSE, MAE, and R². Gradient-weighted Class Activation Mapping (GradCAM) further demonstrated the model’s ability to prioritize meteorologically critical regions, such as cloud edges and solar disk areas, with distinct attention patterns for sunny and cloudy scenarios. The framework’s practical utility was enhanced through deployment in an interactive web-based Graphical User Interface, enabling real-time solar potential simulations for energy operators. By combining attention mechanisms with interpretable design, this work advances short-term solar forecasting accuracy while providing actionable insights for grid management. Future research directions include multi-modal data fusion and hybrid transformer-CNN architectures to improve robustness across diverse climatic conditions.

A digital twin solution for optimizing productivity and energy performance in hybrid ventilated office space

Effect of pre-corrosion on the fatigue performance of direct energy deposited nickel–aluminum bronze (NAB)

Integrating thermal bridge analysis with life-cycle assessment: energy and environmental performance of Ply-Lam CLT envelopes in ZEB-Level apartments

Sensorless dynamic optimization of greenhouse agrivoltaics for energy and crop-aware PV operation

Thermal performance of energy retaining walls in hot-dominant climates: experimental evaluation in unsaturated Brazilian soil

Indoor Environmental and Energy Performance Analysis of Horizontal BIPV Windows and Their Potential to Meet South Korean ZEB Standards

Improving energy performance and futureproofing social housing: Professional views and policy directions in the UK

Thermal and energy performance of a PCM-EAHE system in restricted underground backfill pit trenches

A comparative study of machine learning methods to predict the thermal and energy performance of a small-scale solar chimney

Reliable electricity supply remains a major constraint on economic activity in Nigeria, while large quantities of agricultural residues and processing wastes are generated with limited productive use. This study evaluates the techno-economic feasibility and energy performance of small-scale agricultural waste-to-energy (WtE) pathways suitable for decentralized deployment in Nigeria. Three representative conversion routes are assessed using a harmonized framework: biomass gasification coupled with an internal combustion engine, direct combustion integrated with an Organic Rankine Cycle, and anaerobic digestion with biogas-to-power. A consistent functional unit of one tonne of waste processed (as received) and common system boundaries are applied to enable technology-neutral comparison of specific electricity yield, conversion efficiency, and levelized cost of electricity (LCOE). Base-case scenarios are defined using literature-supported technical and economic parameters relevant to Nigerian agricultural residues. Results indicate that gasification-ICE delivers the highest net electricity yield (574.2 kWh/t) and the lowest LCOE (0.138 USD/kWh) under electricity-only operation, while combustion–ORC produces lower electricity output (311.4 kWh/t) but achieves the highest overall energy utilization when useful heat is recovered in combined heat and power mode. Anaerobic digestion yields lower electricity per tonne (169.2 kWh/t) but demonstrates strong compatibility with wet wastes and high conversion efficiency on a biogas-energy basis. Sensitivity analysis shows that capacity factor and capital cost are the dominant drivers of economic viability, while feedstock moisture content and methane fraction strongly influence energy output. The findings highlight the importance of aligning WtE technology choice with feedstock characteristics, heat demand, and operational conditions, and provide evidence-based guidance for decentralized agricultural waste-to-energy deployment in Nigeria.

Energy, exergy, and environmental performance of a solar dryer for orange slices across tray levels and thicknesses

Hospitals are the most energy-intensive buildings in the tertiary sector because they have continuous and high demand for heating and cooling (to meet strict thermal comfort conditions), hot water, kitchen facilities, electricity, etc. Investigation of the energy performance of hospital buildings is crucial for defining energy savings and developing benchmarks and design guidelines for nearly Zero-Energy Hospitals (nZenHs). This study investigates the energy efficiency of hospital buildings in Greece and the necessary retrofit strategies to transform them to nearly Zero-Energy Buildings (nZEBs). Six building typologies were recognized, based on the building’s floor plan, and energy upgrade scenarios were investigated for each typology. The first scenarios aimed at improving the building’s energy efficiency, and the last one exploited the use of renewable energy source (RES) systems to minimize energy consumption. More specifically, a rooftop photovoltaic system was examined. The results showed differences in hospitals’ energy performance according to typology and climatic zone. They strongly confirm that hospitals can be transformed into buildings with nearly zero-energy consumption, irrespective of their design. The significant energy savings achieved by transforming hospitals into NZEBs highlight the crucial role in enhancing energy efficiency in tertiary sector buildings.

An energy crisis is a significant concern, and solar collectors are among the most efficient energy conversion devices. Inefficient heat transfer is an issue that reduces overall performance. This research investigates the use of hollow semi-stadium fins (HSSF) arranged in a multi-level array with baffles to enhance the system's overall performance. Outdoor testing was undertaken over three days with varying flow rates of 0.01kg/s (Day 1), 0.03kg/s (Day 2), and 0.07kg/s (Day 3). The study considered the temperature of the solar collector, as well as its energy and exergy performance. The study found a thermal efficiency range of 12.99-71.91% and a maximum value of 71.91% corresponding to an irradiance of 800 W/m2 and a flow value of 0.07kg/s. The inlet-to-outlet temperature differential peaked at 21.80°C at 0.01kg/s. The most excellent exergy efficiency was 17.06% at a flow rate of 0.01kg/s, with a range of 0.55% to 17.06% for all irradiances and mass flow rates. Performance results were validated using numerical results and earlier research. Larger flow rates increase thermal efficiency but decrease exergy efficiency. The solar collector with HSSF and baffles highlights its energy and exergy enhancements for sustainable energy applications.

Natural gas-fired combined power plants (NGFCPP) stand out as a promising technology for electricity generation, boasting high conversion efficiency and relatively low carbon dioxide emissions. Numerous researchers have explored diverse strategies to further optimize the performances of these systems. The main aims of this work are to model the 4E (energy, exergy, economic, and environmental) performances of a new design of an NGFCPP and compare them to those of an operating conventional plant (Hadjret Enouss plant). The obtained results show that the plant operating with the new design achieved a higher energy efficiency of 63.77%, compared to the Hadjret Enouss plant's 58.87%. Moreover, its exergy efficiency of 56.58% also surpassed the Hadjret Enouss plant's 55.54%. Although the NPV of the new design was slightly lower at 764.57M€ compared to the Hadjret Enouss plant's 776M€, the developed plant demonstrated superior sustainability with the lowest CO2 emissions at 40.77kg/s and the least cooling water consumption at 5.984m³/s. In conclusion, this new design offers significant long-term benefits, with the potential to save a considerable amount of fuel and reduce environmental impact over its lifetime. For example, over 35 years of operation, the developed plant can save 154.526million kg of natural gas compared to conventional NGFCPPs, leading to an annual reduction in CO2 emissions of approximately 24.28million kg.

Abstract The planning and operation of renewable energy, especially wind power, depend crucially on accurate, timely, and high‐resolution weather information. Coarse‐grid global numerical weather forecasts are typically downscaled to meet these requirements, introducing challenges of scale inconsistency, process representation error, computation cost, and entanglement of distinct uncertainty sources from chaoticity, model bias, and large‐scale forcing. We address these challenges by learning the climatological prior distribution of a target region with a generative model, using its high‐resolution numerical weather simulations. An optimal combination of this learned high‐resolution climatological prior with coarse‐grid large scale forecasts yields highly accurate, fine‐grained, full‐variable, large ensemble of weather pattern forecasts. Using observed meteorological records and wind turbine power outputs as references, the proposed methodology verifies advantageously compared to existing numerical/statistical forecasting‐downscaling pipelines, regarding either deterministic/probabilistic skills or economic gains. Moreover, a 100‐member, 10‐day forecast with spatial resolution of 1 km and output frequency of 15 min takes 1 hr on a moderate‐end GPU, as contrast to CPU hours for conventional numerical simulation. By drastically reducing computational costs while maintaining accuracy, this paradigm paves the way for more efficient and reliable renewable energy planning and operation.

The construction sector is a major driver of economic growth and development; however, its activities, particularly in building construction, contribute substantially to environmental pollution. Roofs, as essential structural components, play a significant role in this environmental impact. This study evaluates six innovative roofing systems-Cobiax, Waffle, Roofix, Hollowcore, Light Composite Panel (LCP) and Contruss-by analyzing their environmental and economic performance.A cradle-to-gate life cycle assessment (LCA) was conducted using SimaPro and GaBi software, applying the CML methods to assess three key impact categories: global warming potential, acidification and eutrophication. We used Impact method 2002 to analyze human health, ecosystem quality, climate change and resources. EnergyPlus was used solely to estimate the operational energy consumption of each roofing system. This calculation does not form part of the cradle-to-gate LCA and was included only to provide additional insights into energy performance. The main LCA analysis remains strictly cradle-to-gate.The economic analysis indicated that the Contruss system is the most cost-effective option, while Cobiax demonstrated superior thermal insulation and lower heat transfer. Sensitivity analysis identified concrete, molding and reinforcement materials as the primary contributors to environmental impacts, suggesting that optimizing or reducing these materials could significantly lower the overall environmental burden of roofing systems in Iran's construction sector.

This paper focuses on the of factors influencing calorific consumption of a vertical roller mill (VRM) and solution proposals to reduce it at a cement factory called X cement factory (for confidentiality reasons). The research aims to identify the main operational, mechanical, and material factors contributing to elevated specific calorific consumption (SCC) and to propose corrective measures for improving energy performance. Data covering a five-month production period are collected from the company’s production reports and analyzes using key performance indicators such as feed rate, moisture content, stoppage frequency, and fuel consumption. Results reveal that increased raw material moisture, frequent operational stoppages, and unstable feed rates are the dominant factors leading to higher SCC. The study concludes by proposing technical and organizational measures such as feed rate stabilization, moisture control, preventive maintenance, and improved combustion management. Implementing these measures can significantly reduce calorific consumption, enhance energy efficiency, and promote sustainable cement production.