Cloudy Days Research Articles

An improved NSGA‐II for the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles and home‐distributed photovoltaic generation

AbstractThis paper investigates four energy utilization scenarios with or without home‐distributed photovoltaic generation (HDPG) to reduce the generation cost and pollutant emission of the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles (DEED‐PEV). The first scenario considers valley filling for the charging of PEVs. The second scenario combines valley filling and peak shaving for the charging and discharging of PEVs. The third scenario adds peak shaving of HDPG to the first scenario, followed by the peak shaving with the discharging of PEVs. The fourth scenario rearranges the distribution of photovoltaic (PV) power for the third scenario, and the PV power in the afternoon is stored by a photovoltaic energy storage system (PESS) and consumed in the evening. A universal procedure is designed for the valley filling and peak shaving of the four scenarios, which is beneficial for determining the filled and shaved loads with respect to certain time intervals. An NSGA‐II method based on a modified crossover and an elimination of individuals (NSGA‐II‐MCEI) is proposed for the multiobjective optimization of DEED‐PEV. The modified crossover can improve the convergence of NSGA‐II‐MCEI, and the elimination operator can maintain the evenness of the nondominated solutions. According to experimental results, scenario 4 achieves cost savings of 95386.62 $, 85636.87 $, and 6776.85 $, respectively, for Scenarios 1, 2, and 3, and it reaches emission reductions of 27617.64 kg, 17252.71 kg, and 220.98 kg, respectively, for scenarios 1, 2, and 3. Also, scenario 4 outperforms the other three scenarios for three weather conditions such as sunny day, cloudy day, and rainy day.

Research on highway rain monitoring based on rain monitoring coefficient.

The real-time and accurate monitoring of severe weather is the key to reducing traffic accidents on highways. Currently, rainy day monitoring based on video images focuses on removing the impact of rain. This article aims to build a monitoring model for rainy days and rainfall intensity to achieve precise monitoring of rainy days on highways. This paper introduces an algorithm that combines the frequency domain and spatial domain, thresholding, and morphology. It incorporates high-pass filtering, full-domain value segmentation, the OTSU method (the maximum inter-class difference method), mask processing, and morphological opening for denoising. The algorithm is designed to build the rain coefficient model Prain coefficient and determine the intensity of rainfall based on the value of Prain coefficient. To validate the model, data from sunny, cloudy, and rainy days in different sections and time periods of the Jinan Bypass G2001 line were used. The aim is to raise awareness about driving safety on highways. The main findings are: the rain coefficient model Prain coefficient can accurately identify cloudy and rainy days and assess the intensity of rainfall. This method is not only suitable for highways but also for ordinary road sections. The model's accuracy has been verified, and the algorithm in this study has the highest accuracy. This research is crucial for road traffic safety, particularly during bad weather such as rain.

Intra-hour solar irradiance forecasting using topology data analysis and physics-driven deep learning

Real-time Direct Normal Irradiance (DNI) prediction is crucial for reliable and economic operation of Concentrated photothermal Solar Power (CSP) system in arid desert areas. However, the stochastic characteristics of short-term multidimensional meteorological time series make intra-hour DNI prediction a challenging task. In this study, we have proposed a deep learning model called TLD, which is combined with topological features captured by Topology Data Analysis (TDA) and temporal features captured by LSTM to address this challenge. Experimental results demonstrated that TLD outperformed the five latest models (Ridge, RF, C_GRU, BiLSTM, and GBRT) on seven solar radiation datasets in arid desert areas. Further analysis revealed that the proportion of cloudy days is a key factor affecting the model's performance. To enhance the forecast ability of TLD, we developed a physics-informed hybrid model named TLDP based on TLD and a smart persistence model, which fully combines the DNI prediction ability of TLD under cloudy conditions and that of the smart persistence model under sunny conditions. Experimental results of eight datasets collected from real-world solar photothermal power stations indicated that TLDP outperformed existing models, which may lay a foundation for more economical and stable operation of CSP plants in arid desert areas.

An Experimental Investigation of the Precipitation Utilization of Plants in Arid Regions.

What represents a water source for the ecological restoration of a plant in an arid region is still up to debate. To address this issue, we conducted an in situ experiment in the Ulan Buh Desert of China, to study desert plants absorbing atmospheric water vapor. We selected Tamarisk, a common drought-salt-tolerant species in the desert, for ecological restoration as our research subject, used a newly designed lysimeter to monitor precipitation infiltration, and a sap flow system to track reverse sap flow that occurred in the shoot, branch, and stem during the precipitation event, and observed the precipitation redistribution process of the Tamarisk plot. The results showed that Tamarisk indeed directly absorbs precipitation water: when precipitation occurs, the main stem, lateral branch, and shoot all show the signs of reversed sap flow, and the reversed sap flow accounted for 21.5% of the annual sap flow in the shoot and branch, and 13.6% in the stem. The precipitation event in the desert was dominated by light precipitation events, which accounted for 81% of the annual precipitation events. It was found that light precipitation can be directly absorbed by the Tamarisk leaves, especially during nighttime or cloudy days. Even when the precipitation is absent, it was found that desert plants can still absorb water from the unsaturated atmospheric vapor; even the absorbed atmospheric water vapor was transported from the leaves to the stem, forming a reversed sap flow, as a reversed sap flow was observed when the atmospheric relative humidity reached 75%. This study indicated that the effect of light precipitation on desert plants was significant and should not be overlooked in terms of managing the ecological and hydrological systems in arid regions.

Evaluation of Medical Students’ Knowledge and Their Adherence to Safety Measures Regarding Solar Ultraviolet Exposure

Ultraviolet radiation, a type of electromagnetic radiation, can be released by both artificial and natural sources, including the sun. It is imperative to adopt the appropriate safety measures when close to an ultraviolet radiation source, as unprotected UV radiation exposure has been associated with a higher risk of developing various illnesses, such as skin tumors. This study aims to evaluate undergraduate medical students' knowledge of potential hazards from UV radiation exposure and their practice of safety measures. A cross-sectional study was conducted at Omar Al-Mukhtar University, Al-Beyda City, Libya. One hundred and nine medical students were involved in this study. The questionnaire examined participants' knowledge of and behavior toward potential risks associated with ultraviolet (UV) radiation exposure. Notably, 77.1% of them were female, and 22.9% were male. The majority of the respondents were more than 22 years old. Above half, 59.6% showed moderate knowledge about UV exposure consequences; most of the students, 91%, knew the side effects of UV on health, followed by 86%, who indicated that UV can cause premature aging and cancer. In contrast, less than a third knew that UV has a negative impact on the immune system, and about two-thirds (73.9%) didn't realize that sunburn can occur even on a cloudy day. Although medical students showed a moderate level of knowledge regarding UV radiation, there was a lack of active sun safety measures.

First estimation of hourly full-coverage ground-level ozone from Fengyun-4A satellite using machine learning

Ground-level ozone (O3), renowned for its adverse impacts on human health and crop production, has garnered significant attention from governmental and public sectors. To address the limitations posed by sparse and uneven ground-level O3 observations, this study proposes an innovative method for hourly full-coverage ground-level O3 estimation using machine learning. Meteorological data from National Centers for Environmental Prediction global forecasting system, satellite data from Fengyun-4 A(FY-4 A) and Ozone Monitoring Instrument, emission inventory from Multi-resolution Emission Inventory for China, and other auxiliary data are utilized as input variables, while ground-based O3 observations serve as the response variable. The method is applied on a monthly basis across China for the year 2022, resulting in the generation of an hourly full-coverage high-resolution (4 km) ground-level O3 estimation, termed ML-derived-O3. Cross-validation results demonstrate the robustness of ML-derived-O3 yielding a coefficient of determination (R 2) of 0.96 (0.91) for sample-based (site-based) evaluations and a root-mean-square error (RMSE) of 9.22 (13.65) µg m−3. However, the date-based evaluation is less satisfactory due to the imbalanced training data, resulting from the pronounced daily variations in ground-level O3 concentrations. Nevertheless, the seasonal and hourly ML-derived-O3 exhibits high prediction accuracy, with R 2 values surpassing 0.95 and RMSE remaining below 7.5 µg m−3. This study marks a significant milestone as the first successful attempt to obtain hourly full-coverage ground-level O3 data across China. The diurnal variation of ML-derived-O3 demonstrates high consistency with ground-based observations, irrespective of clear or cloudy days, effectively capturing ground-level O3 pollution exposure events. This novel estimation method will be employed to establish a long-term high spatial-temporal resolution ground-level O3 dataset, which holds valuable applications for air pollution monitoring and environmental health research in future endeavors.

Development and performance assessment of a hybrid solar cabinet dryer for fish drying

The fish products can be stored for longer period in dry condition, but most of the fresh fish in remote areas of our country are spoils due to lack of proper preservation. To overcome this problem, hybrid solar dryer can play an important role to make drying continuous during night and daytime also off sunshine hours. In this project, a hybrid solar cabinet dryer has been designed, constructed, and performance tested for fish drying. The dryer consists of a drying chamber, solar collector, two trays of dimensions (60×60) cm and paraffin wax as PCM. It also consists of photovoltaic cell modules, heating element, electric battery, and other electric equipment such as inlet and outlet fans. The dryer has been operated as both a solar energy dryer during normal sunny days and a hybrid solar dryer during cloudy days. Paraffin wax system inside drying chamber absorbed heat energy in Sunny time. At the same time the battery is charged from the solar system and therefore the water kept in the tank reaches a certain temperature by absorbing heat from nature, which provides heat by circulating the water inside the drying chamber during inclement weather. Electric fans are used at the inlet of the solar collector and outlet of the dryer to maintain adequate air flow inside the drying chamber. During the night and cloudy weather paraffin wax and heater system deliver heat to maintain the set temperature in drying chamber. The grain bed in the dryer is heated by air that comes from a separate solar collector, and at the same time, the drying cabinet collects solar energy directly through the transparent walls and roof. The dryer demonstrated the capacity to dry fish to an appropriate moisture level in a reasonable amount of time, and concurrently, it insured that the fish would be of high quality. The drying rate of hybrid solar dryers is evaluated on fresh fish and compared with solar dryers and sun drying under the same climatic conditions.

Performance optimization of photovoltaic system under real climatic conditions using a novel MPPT approach

ABSTRACT In order to mitigate the impact of temperature and irradiation variations on photovoltaic (PV) panels, it is crucial to employ the maximum power point tracking (MPPT) technique. This paper introduces a novel MPPT approach that provides a reference current, denoted as Iref, to extract the maximum power from the PV array. The difference between the input current Ipv, and Iref is then fed into a PID controller, which adjusts the duty cycle accordingly to control the Quadratic Boost Converter (QBC). The proposed MPPT approach is based on the analysis of measurement data obtained from PV panels. From this, a linear mathematical function between Iref and solar irradiation is achieved. To assess the performance of this approach, a comparative study is being carried out not only with conventional MPPT techniques such as P&O and INC-COND but also with modern techniques such as ANN, PSO, FLC, and improved P&O (IMP-P&O). The simulation results clearly demonstrate enhanced tracking performance under various test conditions, including the Step & Ropp test, as well as real profiles for both sunny and cloudy days. Certainly, the novel approach shows its ability to outperform both conventional and modern methods with a faster convergence speed (less than 2 milliseconds), the lowest steady-state oscillations (less than 0.25 Watts), and the highest tracking efficiency (greater than 99.79%).

An efficient day-ahead cost-based generation scheduling of a multi-supply microgrid using a balancing composite motion optimization (BCMO) approach

The efficient management and optimal scheduling of generation resources in multi-supply microgrid (MG) are essential for ensuring cost-effective and reliable power supply. Renewable energy resources such as wind turbines (WTs), photovoltaic (PV) systems, micro turbine (MT) batteries, and fuel cells (FCs) are utilized. This paper presents Balancing Composite Motion Optimization (BCMO) for day-ahead cost-based generation scheduling in multi-supply MG. The BCMO approach combines the advantages of composite motion optimization techniques to minimize the overall generation cost. The proposed method considers demand forecasts, renewable energy availability, and real-time pricing to optimize generation schedule. The microgrid is modeled as mathematical optimization problem, considering multiple generation sources, including renewable energy, conventional generators, and energy storage systems, that are used to optimize the above factors. The objective function is formulated to minimize the total generation cost while satisfying demand requirements and operational constraints. By considering the cost of generation from different sources and availability of renewable energy, the proposed approach enables the microgrid to operate economically efficiently. The objective function is formulated to minimize the total generation cost while satisfying the demand requirements and operational constraints. By considering the cost of generation from different sources and the availability of renewable energy, the proposed approach enables the microgrid to operate in an economically efficient manner. The performance of the proposed system is executed on the MATLAB working platform and compared with various approaches. The best BCMO solution in cold sunny, warm sunny, cold cloudy days and warm cloudy are 290.8821, 358.7908, 261.324, and 2265.055., respectively.

Diurnal, Seasonal, and Vertical Changes in Photosynthetic Rates in Cinamomum camphora Forests in Subtropical China

The increase in the global atmospheric CO2 concentration is expected to increase the productivity of forests, but the dynamic processes of such increased productivity in the forest canopy remain unclear. In this study, diurnal and seasonal variations and vertical changes in photosynthetic rates were investigated in Camphor tree (Cinnamomum camphora) forests in subtropical China. The effect of photosynthetically active radiation (PAR) and CO2 concentrations on photosynthetic rates were also examined in the studied forests. Results showed the diurnal patterns of photosynthesis exhibited two peaks on sunny days, but only one peak on cloudy days. The daily average photosynthetic rate on cloudy days was approximately 74% of that on sunny days. The photosynthetic rate decreased along the vertical forest canopy profile. If the photosynthetic rate in the upper canopy layer was 100%, the corresponding rates were 83% and 25% in the middle and lower canopy layers, respectively. The rates of dark respiration derived from the PAR response curve were 1.73, 1.25, and 1.0 µmol m−2 s−1 for the upper, middle, and lower canopy layers, respectively. The apparent quantum yield of photosynthesis was 0.0183, 0.0186, and 0.0327 µmol CO2 µmol−1 PAR for the upper, middle, and lower canopy, respectively. The initial slope of the photosynthetic response curve to CO2 was highest in the upper canopy and lowest in the lower canopy. The seasonal variation in photosynthetic rates exhibited a two-peaked pattern at all canopy positions, with the two peaks occurring in June and September. The stand biomass and biomass carbon storage were 144.7 t ha−1 and 71.6 t C ha−1 in the examined forests, respectively. The study provides a scientific reference for future research on accessing carbon sequestration and designing forest management practices, specifically in regulating canopy structure in subtropical regions.

Impact of a High Penetration of Electric Vehicles and Photovoltaic Inverters on Power Quality in an Urban Residential Grid Part I – Unbalance

This paper presents a measurement-based assessment of the impact of a high penetration of plug-in electric vehicles with on-board charger (EVC) and photovoltaic inverters (PVI) on Power Quality in a public low voltage (LV) network. The paper consists of two parts. This first part is focused on the impact on unbalance, the second part is related to harmonics. The results are based on extensive measurements in an urban residential grid with almost 100 households (HH), 43 PVIs and 36 EVCs. The impact of PVI and EVC on unbalance at different levels of aggregation is described in detail. At first the individual impact of a single PVI and EVC is discussed for different operating conditions. In particular, a sunny day, a cloudy day and hours during the night are used for PVI, while periods of charging and periods of no charging are considered for EVC. Next sections analyse the interaction between PVI, EVC and residual load at household level as well as the overall impact of PVIs and EVCs in the low voltage grid. The analysis considers current unbalance and voltage unbalance.

Factors affecting the daytime cooling effect of cool materials: A case study combining experiment and simulation

Cool materials, with their high reflectivity, can significantly reduce the heat absorbed by buildings in summer and thus reduce energy consumption. The amount of solar radiation received by building surfaces varies with their orientation, so the effectiveness of cool materials on different building surfaces may vary. This study investigated the effects of using cool materials on scaled-down building models and full-scale real buildings through a combination of experiments and simulations, considering factors including building surfaces of different orientations, material properties, and weather conditions. Both experimental measurements and simulation studies showed that applying cool materials on roofs has the best cooling effect, reducing indoor temperatures by 0.93 °C in full-scale models. The south and west façades also showed good cooling effects when using cool materials. The north façade has the least cooling effect when using cool materials. Compared to exposed cement boards and regular materials, cool materials showed significantly better cooling effects, similar to those of 2 cm thick XPS insulation boards. Overall, buildings with poorer thermal insulation performance benefit more from the cooling effect of cool materials in summer. Additionally, the cooling effect of cool materials is ineffective on cloudy and rainy days. We found that the cooling effect of cool materials was significantly reduced when the solar radiation was less than 400 W/m2. The findings of this study provide guidance for the use of cool materials in energy retrofitting of existing buildings and the construction of low-carbon cities.

A Daytime Smoke Detection Method Based on Variances of Optical Flow and Characteristics of HSV Color on Footage from Outdoor Camera in Urban City

In order for detection of a fire in fields, it is effective to detect smoke since it often behaves as a precursor of the fire. One preferable way for early detection is to use visual information from outdoor cameras that widely monitor the filed. There have been many attempts to detect smokes via optical sensors on digital cameras using optical flow methods, but not fully successful from practical-use aspects. It is because the area of smokes occupying on the footage by outdoor cameras is not necessarily large enough. Moreover, in case of urban cities, discrimination of the smokes from other moving objects such as cars, trees and turbines is not easy. Herein we propose a novel method to detect daytime smokes based on variance of optical flow and characteristics of HSV (hue-saturation-value) color. We apply the method to a set of footage of three days obtained in an industrial zone in Japan. Successful results are obtained as over 90% of smokes are detected. Notable is that this method is independent of solar radiation conditions on sunny and cloudy days.

Diatomite-Based Recyclable and Green Coating for Efficient Radiative Cooling.

Radiative cooling is a promising strategy to address energy challenges arising from global warming. Nevertheless, integrating optimal cooling performance with commercial applications is a considerable challenge. Here, we demonstrate a scalable and straightforward approach for fabricating green radiative cooling coating consisting of methyl cellulose matrix-random diatomites with water as a solvent. Because of the efficient scattering of the porous morphology of diatomite and the inherent absorption properties of both diatomite and cellulose, the aqueous coating exhibits an excellent solar reflectance of 94% in the range of 0.25-2.5 μm and a thermal emissivity of 0.9 in the range of 8-14 µm. During exposure to direct sunlight at noon, the obtained coating achieved a maximum subambient temperature drop of 6.1 °C on sunny days and 2.5 °C on cloudy days. Furthermore, diatomite is a naturally sourced material that requires minimal pre-processing, and our coatings can be prepared free from harmful organic compounds. Combined with cost-effectiveness and environmental friendliness, it offers a viable path for the commercial application of radiative cooling.

Effects of urban vegetation on microclimate and building energy demand in winter: An evaluation using coupled simulations

As an important landscape, the evergreen vegetation is prevailing, even in cold region cities. However, the impacts of promoting vegetation on microclimate and building heating load (BHL) are still inexplicit. This study validated the feasibility of a new-developed coupled model that linked ENVI-met and EnergyPlus, for predicting the microclimate and BHL in winter. The impacts of vegetation configuration changes on the performances of three typical urban blocks in Xi'an, a high-density city in the cold region of China, were then evaluated. When the leaf area index of green lands increased from 0.79 to 4.77 m2/m2, a decrease of 3.27 °C on mean radiant temperature, a decrease of 0.48 m/s on wind speed, and an increase of 0.47 °C on air temperature at the block scale could be observed. To the three blocks, the BHL of thermal zones in the height range of tree canopy increased by 7.16 %, 1.52 % and 3.57 % during a sunny day, and decreased by 2.80 %, 0.55 %, and 0.06 % during a cloudy day maximally. Overall, the evergreen vegetation produced negative impacts on the building energy efficiency of cold region cities. For urban planners, it's advisable to concentrate evergreen plants in high-rise blocks to mitigate such disadvantages.

Smart Flexible Porous Bilayer for All‐Day Dynamic Passive Cooling

Radiative cooling and evaporative cooling are sustainable cooling techniques without additional energy input. However, radiative cooling lacks dynamic cooling ability, while evaporative cooling demands external water replenishment, hindering their applications. Herein, a smart radiative/evaporative cooling bilayer combining a polydimethylsiloxane (PDMS) upper layer with a hydrogel lower layer is presented for efficient all‐day dynamic passive cooling. The PDMS layer with high solar reflectivity (0.930) and emissivity (0.952) provides excellent all‐day radiative cooling and protects the hydrogel from solar exposure, while the hydrogel layer demonstrates remarkable water evaporation and absorption, achieving dynamic evaporative cooling. Thus, the synergy of the two layers significantly enhances the overall cooling performance. Specifically, the bilayer can achieve the peak cooling power values of 424.4 and 650.6 W m−2 as well as the maximum subambient cooling temperatures of 10.4 and 3.7 °C during sunny and cloudy mid‐days, respectively. Moreover, the bilayer obtains 3.2 °C warmer temperature compared with the PDMS alone during cold nighttime, while the two structures exhibit comparable cooling performance during hot nighttime, indicating the self‐adaptive cooling property of the bilayer. In addition, the bilayer can achieve good cooling performance even under continuous cloudy days, offering a promising strategy for efficient all‐day dynamic passive cooling.

A hybrid machine-learning model for solar irradiance forecasting

Abstract Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic (PV) power plants. These plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power output. As the generation capacity within the electric grid increases, accurately predicting this output becomes increasingly essential, especially given the random and non-linear characteristics of solar irradiance under variable weather conditions. This study presents a novel prediction method for solar irradiance, which is directly in correlation with PV power output, targeting both short-term and medium-term forecast horizons. Our proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model. The proposed method excels in forecasting solar irradiance, especially during highly intermittent weather periods. A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable framework. We evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford, USA and compared it against three forecasting models: persistence, modified 24-hour persistence and least squares. Based on three widely accepted statistical performance metrics (root mean squared error, mean absolute error and coefficient of determination), our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.

Multilevel and general linear modeling of weather and time effects on the emotional and behavioral states of children with profound intellectual and multiple disabilities.

Eliciting the emotional and behavioral states of children with severe or profound intellectual disabilities (IDs) and profound intellectual and multiple disabilities (PIMD) due to their complex and atypical developmental trajectories has become increasingly elusive. It is evident that the environment, influenced by weather conditions and time of the day, plays a pivotal role in molding children's behaviors, emotions, and interactions. This underscores the significance of the environment as a critical factor in exploring the communication dynamics of children with PIMD/IDs. Over five months during fall and winter seasons, we conducted 105 video-recorded sessions with 20 children aged 8 to 16 with PIMD/IDs. These sessions aimed to capture the emotional and behavioral states interpreted by caregivers while simultaneously collecting indoor and outdoor weather indices, location, and time data. Using cross-classified multilevel and general linear models adjusted for individual characteristics and location variability with subsequent simple slope analyses, we examined the main and seasonal interaction effects of indoor and outdoor weather indices and time of the day on the emotional and behavioral states of children with PIMD/IDs. The models revealed that higher atmospheric pressure (atm), indicative of pleasant and favorable weather conditions, was associated with increased engagement (indoor: p < 0.01; outdoor: p < 0.01) and interest (outdoor: p < 0.01) behaviors. In contrast, engagement levels decreased before lunchtime (p < 0.01; p < 0.001), and inclement or unstable weather conditions characterized by low-pressure systems (p < 0.05) and stronger wind speed (p < 0.05) led to more refusal or disagreement. During winter, children displayed significantly more agreement with their caregivers (p < 0.001). Interestingly, they also engaged more on cloudy days (p < 0.05). Furthermore, simple slope analyses revealed that high atm conditions in fall were linked to more engagement (p < 0.05) while humid conditions predicted more assent behaviors (p < 0.001). However, cloudy weather predicted less attentional focusing (p < 0.05) and interest (p < 0.01) behaviors in winter. This study confirms that fluctuations in weather indices, including seasonal changes and time of the day, can provide potential pathway indicators and supplement behavioral observations to elicit the behavioral states of children with PIMD/IDs. These findings highlight the importance of considering these factors when designing meaningful interactions and communication interventions for this population.

Functionalized Superhydrophobic Coatings with Electro‐Photothermal Effect for All‐Day Durable Anti‐Icing

AbstractSuperhydrophobic surfaces offer notable advantages, including markedly low water affinity and reduced ice adhesion strength. Nevertheless, their practical utility is impeded by their limited durability and vulnerability to failure in cold and humid environments. In this study, a novel approach for devising an electro‐photothermal superhydrophobic (EPS) nanocomposite coating is presented. The findings indicate that the EPS nanocomposite coating exhibits both physical and chemical self‐cleaning attributes, showcasing a synergistic interplay of superhydrophobicity, electrothermal, and photothermal characteristics. The superhydrophobic coating delays icing about four times longer than the original coating. At ambient temperatures of −20 °C, the coating stacked with an electro‐ and photo‐thermal performance de‐icing layer reduces the de‐icing time by about 5 times more than the purely photo‐thermal performance de‐icing time, and reduces the de‐icing time by about 4 times more than the purely electro‐thermal de‐icing time. Furthermore, the EPS surface demonstrates the capability to sustain temperatures above 0 °C through the photothermal effect on sunny days, utilizing both the electrothermal and photothermal effects on cloudy days, and relying on the electrothermal effect during cold nights. The research introduces a novel method for fabricating functional materials, pertinent to practical anti‐icing and de‐icing applications.

Forecasting Occupants’ Presence and Photovoltaic Power using an Enhanced Stacking Algorithm for Demand/Supply Energy Management

This paper proposes an improved stacking algorithm based on an ANN (S-ANN) model to predict occupants’ presence probability and solar photovoltaic (PV) power generation over a 24-hour horizon for demand and supply energy management. The proposed S-ANN improves on traditional stacking by training each base model on various datasets instead of the same dataset. Python is employed as the simulation software for deploying the S-ANN model and conducting the analyses. The model’s performance is evaluated using the Normalized Root Mean Square Error (NRMSE) and Normalized Mean Absolute Error (NMAE) metrics. The results establish the effectiveness of the proposed model with an NRMSE of 2.91% and NMAE of 2.20% on a cloudy day (prediction of occupant presence in the kitchen reported as an example).