Dust deposition on photovoltaic (PV) panels causes power losses, and its spatial nonuniformity reduces the effectiveness of fixed-intensity cleaning. This study develops a near-infrared (NIR) vision-guided adaptive electrostatic cleaning method that converts local soiling information into zonal voltage control. NIR images were segmented using a U-Net model, transformed into a dust-coverage matrix, and mapped to dynamic voltages during electrode scanning. The U-Net model achieved an accuracy of 0.9936, a precision of 0.9629, a recall of 0.8842, an F1 score of 0.9219, and an intersection over union (IoU) of 0.8551. Under fixed-voltage cleaning, increasing dust density from 5 to 15 g m-2 reduced dust removal efficiency from 98.79% to 84.59%, while nonuniform soiling further increased local residue. Relative to the corresponding fixed-voltage results obtained under the same dust-loading and soiling-pattern conditions, the adaptive mode reduced residual soiling by 8.03%-32.76% and improved power recovery by 10.60%-34.17% under patchy soiling. Across striped, edge, and patchy soiling at the three dust-loading levels, it reduced the energy consumption per unit recovery gain by 37%-68%. The proposed method links visual soiling recognition with localized electrostatic actuation, offering a water-free and energy-efficient route for intelligent PV cleaning.
This research paper explores the feasible integration of free cooling shallow pond water with the radiant system of buildings equipped with radiant embedded infrastructure in the tropical savanna climatic region to minimize the operative temperature of the indoors. The system represents an innovative configuration of a pond water radiant heating/cooling system with a shallow pond water test rig to vary forward water temperatures, thereby analyzing the impact on the thermal performance of interior surfaces during the peak summer times when the cooling load is at its maximum. The purpose of the experimentation was to assess the impact of the radiant surface for various supply water temperatures by executing (i) the radiant system in the standalone mode, (ii) operating the radiant system along with the ceiling fan, and (iii) operating the radiant system along with the operating FCU. Two similar building envelopes were constructed to test the system performance; one was traditional, while the other was equipped with encapsulated pipes. The surface temperature, surface heat flux, air temperature, and weather conditions were measured continuously. Operating the radiant system with the FCU resulted in an operative temperature range of 26.8-29.95 degrees C, which was nearly 8 degrees C less than the highest operative temperature of the traditional building.
This study presents a coupling optimization model for a geothermal fluid-driven Organic Rankine Cycle power generation system that integrates thermal and economic performance. The investigation begins with optimizing the evaporation temperatures of six working fluids, followed by adjusting the condensation temperature to enhance the system's thermal efficiency. Additionally, a comprehensive economic analysis of the total investment and component-level cost was conducted. The results demonstrate that both increasing the evaporation temperature and decreasing the condensation temperature can improve the system's thermal performance. and reduce operational expenses. By comprehensively considering the system net output power (W net) and unit cost (C i), and adopting the multi-objective optimization strategy coupling the NSGA-II algorithm and entropy weight TOPSIS method, it is determined that R245fa is selected as the optimal working fluid under the geothermal fluid temperature of 140 degrees C, with an optimal evaporation temperature of 118 degrees C and a condensation temperature of 8 degrees C. Compared to the current system, adopting R245fa increases the net output power by 46.6% while reducing the unit cost (C i) by 50.2%. The total investment cost of the system rises by 15.23%, with 79% of the additional cost concentrated on the evaporator and condenser, providing valuable guidance for practical retrofit projects.
Recently, the usage of Electric Vehicles (EVs) has tremendously increased due to the development of green cars in the automotive sector. One of the most important components in addressing monitoring and safety issues in an EV is the battery. Specifically, assessing the state of charge (SoC) and State of Health (SoH) is crucial in battery-only EVs. Lithium-ion batteries are now the most popular energy source for electric cars. The safety and dependability of EV operation are ensured by monitoring the battery life and maintaining it using a Battery Management System (BMS). To calculate an EV battery's SoC and SoH, a novel ElectroPredictNet is developed to estimate the battery life by assessing the BMS parameters (SoC and SoH). For this process, pre-processing and feature smoothing phases are added, and five models are explored, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Deep Neural Network (DNN), and R-squared (R2) regression with a self-attention mechanism. These models are applied to accurately estimate battery performance by analyzing charge-discharge cycles and predicting degradation patterns. Each model is evaluated for accuracy and error metrics to determine which model forecasts SoC and SoH best. It also provides valuable insights for enhancing BMS in EVs, achieving a Mean Squared Error (MSE) of 0.002 and a Mean Absolute Error (MAE) of 0.0096. Lastly, a comparative study is conducted with the State-of-The-Art (SOTA) models to validate the effectiveness of the developed ElectroPredictNet in predicting SoC and SoH.
