<div><div><div><div>The identification and prediction of the daily soiling profiles of a photovoltaic site is essential to plan the optimal cleaning schedule. In this article, we analyze and propose various methods to extract and generate photovoltaic soiling profiles, in order to improve the analysis and the forecast of the losses. New soiling rate extraction methods are proposed to reflect the seasonal variability of the soiling rates and, for this reason, are found to identify the most convenient cleaning day with the highest accuracy for the investigated sites. Also, we present an approach that could be used to predict future soiling losses through the implementation of stochastic weather generation algorithms whose ability to identify in advance the best cleaning schedule is also successfully tested. The methods presented in this article can optimize the operation and maintenance schedule and could make it possible, in the future, to predict soiling losses through analysis based only on environmental parameters, such as rainfall and particulate matter, without the need of long-term soiling data.</div></div></div></div>
", "keywords": ["Optimization", "Power", " Energy and Industry Applications", "Schedules", "Rain", "Cleaning", "Field Performance", "solar energy", "0211 other engineering and technologies", "02 engineering and technology", "Prediction methods", "7. Clean energy", "13. Climate action", "Soil measurements", "time series analysis", "0202 electrical engineering", " electronic engineering", " information engineering", "soiling", "stochastic processes", "Data mining", "Photovoltaic systems", "field performance; optimization; photovoltaic (PV) systems; prediction methods; soiling; solar energy; stochastic processes; time series analysis"]}, "links": [{"href": "https://iris.uniroma1.it/bitstream/11573/1625584/3/Micheli_postprint_Extracting_2020.pdf"}, {"href": "http://xplorestaging.ieee.org/ielx7/5503869/8939133/08880477.pdf?arnumber=8880477"}, {"href": "https://doi.org/10.1109/jphotov.2019.2943706"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Journal%20of%20Photovoltaics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/jphotov.2019.2943706", "name": "item", "description": "10.1109/jphotov.2019.2943706", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/jphotov.2019.2943706"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-11-03T00:00:00Z"}}, {"id": "10.3929/ethz-b-000278733", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:19Z", "type": "Journal Article", "created": "2018-07-06", "title": "Cost\u2013benefit optimization of structural health monitoring sensor networks", "description": "Structural health monitoring (SHM) allows the acquisition of information on the structural integrity of any mechanical system by processing data, measured through a set of sensors, in order to estimate relevant mechanical parameters and indicators of performance. Herein we present a method to perform the cost\uffe2\uff80\uff93benefit optimization of a sensor network by defining the density, type, and positioning of the sensors to be deployed. The effectiveness (benefit) of an SHM system may be quantified by means of information theory, namely through the expected Shannon information gain provided by the measured data, which allows the inherent uncertainties of the experimental process (i.e., those associated with the prediction error and the parameters to be estimated) to be accounted for. In order to evaluate the computationally expensive Monte Carlo estimator of the objective function, a framework comprising surrogate models (polynomial chaos expansion), model order reduction methods (principal component analysis), and stochastic optimization methods is introduced. Two optimization strategies are proposed: the maximization of the information provided by the measured data, given the technological, identifiability, and budgetary constraints; and the maximization of the information\uffe2\uff80\uff93cost ratio. The application of the framework to a large-scale structural problem, the Pirelli tower in Milan, is presented, and the two comprehensive optimization methods are compared.
", "keywords": ["Stochastic Processes", "structural health monitoring", "structural health monitoring; Bayesian inference; cost\u2013benefit analysis; stochastic optimization; information theory; Bayesian experimental design; surrogate modeling; model order reduction", "Chemical technology", "Cost-Benefit Analysis", "Bayesian inference", "Bayesian experimental design", "Uncertainty", "Bayes Theorem", "TP1-1185", "02 engineering and technology", "stochastic optimization", "Bayesian experimental design; Bayesian inference; Benefit analysis; Cost; Information theory; Model order reduction; Stochastic optimization; Structural health monitoring; Surrogate modeling; Algorithms; Monte Carlo Method; Nonlinear Dynamics; Stochastic Processes; Uncertainty; Bayes Theorem; Cost-Benefit Analysis; Analytical Chemistry; Atomic and Molecular Physics", " and Optics; Biochemistry; Instrumentation; Electrical and Electronic Engineering", "Article", "surrogate modeling", "0201 civil engineering", "Nonlinear Dynamics", "model order reduction", "cost\u2013benefit analysis", "Monte Carlo Method", "Algorithms", "information theory"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/18/7/2174/pdf"}, {"href": "https://re.public.polimi.it/bitstream/11311/1085132/1/Sensors_2018b.pdf"}, {"href": "https://doi.org/10.3929/ethz-b-000278733"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3929/ethz-b-000278733", "name": "item", "description": "10.3929/ethz-b-000278733", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3929/ethz-b-000278733"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-07-06T00:00:00Z"}}, {"id": "10.5061/dryad.rbnzs7hm4", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:37Z", "type": "Dataset", "created": "2024-05-30", "title": "Data from: Mycorrhizal symbiosis increases plant phylogenetic diversity and regulate community assembly", "description": "unspecifiedFiled survey and data collection This study utilized field survey data collected from 1315 sites across various grassland ecosystems in China. \u00a0Vegetation surveys were conducted during the peak plant growth season, specifically from mid-July to August in middle and high latitude regions, including the Qinghai-Tibetan Plateau, and from August to September in desert, subtropical, and tropical regions.\u00a0 At each site, plant community data was collected using ten 1m \u00d7 1m quadrats (reduced to 0.5m \u00d7 0.5m for meadow and alpine meadow) located randomly within a 100m \u00d7 100m area.\u00a0 \u00a0For shrubland ecosystems, five 5m \u00d7 5m quadrats were randomly placed within the same 100m \u00d7 100m area.\u00a0 Five of the ten quadrats (or all five quadrats in shrublands) were randomly selected for detailed vegetation analysis. Within these selected quadrats, plant species richness (SR) and species relative abundance were recorded.\u00a0 Soil samples were collected from each quadrat using a soil core method at a depth of 20 cm. \u00a0Soil organic carbon content (SOC) were measured for each sample.\u00a0 Total phosphorus (TP), total nitrogen (TN), and soil pH were interpolated from the Basic soil property dataset of high-resolution China Soil Information Grids (2010-2018). To facilitate analysis and interpretation, the 1315 field sites were classified into four distinct ecosystem types: meadow (further categorized into lowland meadows (LM), mountain meadows (MM), and alpine meadows (AM)), steppe (divided into temperate steppe (TS) and alpine steppe (AS)), shrubland (classified as warm shrubland (WG) and tropical shrubland (TG)), and desert (DS). \u00a0This classification was based on plant community composition, climate, and prevailing environmental conditions. Mean annual temperature (MAT, \u00b0C), mean annual precipitation (MAP, mm), and mean diurnal range (mean of monthly maximum temperature - minimum temperature, \u00b0C) for each site were obtained from the WorldClim data layers (specifically, bio_1 and bio_12) at a spatial resolution of 30 seconds \u00d7 30 seconds (approximately 1 km \u00d7 1 km at the equator) (http://www.worldclim.org/). Remote sensing data, including Fraction of Photosynthetically Active Radiation (Fpar) were obtained from MOD15A2H Version 6 data product, slope and elevation data was extracted from the STRM 90m dataset 171 in China, based on the SRTM V4.1 database (https://www.resdc.cn/data.aspx?DATAID=123).\u00a0 Fpar is defined as the fraction of incident photosynthetically active radiation, 400-700 nanometers (nm), absorbed by the green elements of a vegetation canopy. Plant mycorrhizal status and community mycorrhizal index To quantify the mycorrhizal status of each plant community, we calculated a mycorrhizal index representing the degree of potential mycorrhizal colonization within the community. \u00a0The mycorrhizal status of each plant species was determined using an established database of mycorrhizal associations. \u00a0To minimize potential errors during the matching process, species were matched based on their genus level (Brundrett & Tedersoo 2019). The dominant mycorrhizal status of each community was determined based on the mycorrhizal status exhibiting the highest abundance within that community. \u00a0This approach allowed us to differentiate communities based on the predominant mycorrhizal association of their constituent plant species. Construction of phylogenetic relationships and calculation of phylogenetic distances Phylogenetic relationships and distances between plant species were determined using the V. PhyloMaker package and picante package in R. V. PhyloMaker generated phylogenetic hypotheses for the 1235 plant species in our study by linking them to the 'GBOTB.extended' megatree. \u00a0This megatree encompasses 74,531 species, representing all families of extant vascular plants, and serves as the largest dated phylogeny for vascular plants.\u00a0 Phylogenetic distances within each community were calculated using the picante package. \u00a0We calculated two metrics: mean pairwise distance (MPD) and mean nearest taxon distance (MNTD). \u00a0To account for the relative abundance of each species within the community, species abundance was incorporated as a weighting factor in the phylogenetic distance calculations. To mitigate the influence of species richness on community phylogenetic distances, standardized effect size metrics for MPD (SESMPD) and MNTD (SESMNTD) were calculated. \u00a0This standardization involved generating a null distribution by randomly shuffling the distance matrix labels across all taxa 999 times. \u00a0The mean of the null distribution was then used to calculate the standardized effect size. \u00a0SES values less than 0 indicate phylogenetic clustering, where species within the community are more closely related than expected by chance. \u00a0Conversely, SES values greater than 0 indicate phylogenetic overdispersion, where species are more distantly related than expected. Statistical analyses Comparison of plant community composition.\u00a0 To compare plant community composition across different dominant mycorrhizal status, we first examined the relative abundance of different plant families within each of the community types.\u00a0 We then performed non-metric multidimensional scaling (NMDS) using the metaMDS function in the vegan package, based on a Bray-Curtis distance matrix.\u00a0 The stress function value was assessed to ensure model reliability.\u00a0 Significant differences between groups were determined using Adonis, with significance levels denoted as follows: n.s. (not significant), *p < 0.05, **p < 0.01, and ***p < 0.001. Assessing the influence of mycorrhizae on species richness and phylogenetic diversity.\u00a0 To evaluate the role of mycorrhizae in shaping community species richness and phylogenetic diversity, we constructed a set of predictor variables encompassing climate (MAT, MAP, MDR, Fpar), geographic factors (longitude, latitude, elevation, slope), mycorrhizal status (MI), and soil properties (TN, TP, SOC, pH). Multiple regression models were developed using the MuMIn package in R to assess the effects of these predictors on species richness and phylogenetic diversity.\u00a0 A full set of models incorporating all predictors was generated and ranked based on the Akaike information criterion (AIC) using maximum likelihood estimation.\u00a0 Models with \u0394AIC < 2 were retained, and model averaging was employed to calculate parameter estimates and p-values.\u00a0 The relative effect of each predictor was determined by calculating the ratio of its parameter estimate to the sum of all parameter estimates.\u00a0 To further emphasize the importance of mycorrhizae in predicting species richness and phylogenetic diversity, we constructed comparative models excluding the mycorrhizal index while retaining all other predictors.\u00a0 These reduced models were compared to their corresponding full models using AICc values, with lower AICc values indicating superior model performance. Quantifying the relative importance of stochastic and deterministic processes in community assembly.\u00a0 To assess the influence of mycorrhizae on community assembly processes, we utilized normalized stochasticity ratio (NST) analysis.\u00a0 NST, an extension of the Beta-diversity metric, quantifies the relative contribution of stochastic and deterministic processes in community assembly.\u00a0 NST values range from 0 to 1, with 0.5 representing an equal contribution of both processes.\u00a0 NST values predominantly above 0.5 indicate dominance of stochastic processes, while values below 0.5 suggest a greater influence of deterministic processes. We further investigated the impact of different mycorrhizal plant types on community assembly using phylogenetic bin-based null model analysis (iCAMP) (Ning et al. 2020). \u00a0This method, implemented using the iCAMP package in R, divides plant species into phylogenetic bins with significant phylogenetic signals and quantifies the contribution of each bin to deterministic (homogeneous selection (HoS), heterogeneous selection (HeS)) and stochastic (dispersal limitation (DL), homogenizing dispersal (HD), and drift (DR)) processes. \u00a0We selected the 300 most abundant plant species (representing 91.6% of total individuals), assigned them to phylogenetic bins, and determined the dominant mycorrhizal status within each bin. \u00a0The relative contribution of each bin to different assembly processes was then assessed to quantify the influence of different mycorrhizal plant types on ecological processes.", "keywords": ["deterministic processes vs. stochastic processes", "Community assembly", "phylogenetic dispersion", "FOS: Biological sciences", "phylogenetic diversity", "community composition"], "contacts": [{"organization": "Zhang, Entao, Wang, Yang, Chen, Shiping, Zhou, Daowei, Shangguan, Zhouping, Huang, Jianhui, He, Jin-Sheng, Wang, Yanfen, Sheng, Jiandong, Tang, Lisong, Li, Xinrong, Dong, Ming, Yan, Yan, Hu, Shuijin, Bai, Yongfei,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.rbnzs7hm4"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.rbnzs7hm4", "name": "item", "description": "10.5061/dryad.rbnzs7hm4", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.rbnzs7hm4"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-06-03T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Stochastic+Processes&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Stochastic+Processes&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Stochastic+Processes&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Stochastic+Processes&offset=3", "hreflang": "en-US"}], "numberMatched": 3, "numberReturned": 3, "distributedFeatures": [], "timeStamp": "2026-07-26T12:00:13.436090Z"}