scholarly journals An Improved Mathematical Model for Computing Power Output of Solar Photovoltaic Modules

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Abdul Qayoom Jakhrani ◽  
Saleem Raza Samo ◽  
Shakeel Ahmed Kamboh ◽  
Jane Labadin ◽  
Andrew Ragai Henry Rigit
Keyword(s):  
Mathematical Model ◽  
Numerical Methods ◽  
Power Output ◽  
Time Series Data ◽  
Meteorological Data ◽  
Lambert W Function ◽  
Series Data ◽  
Photovoltaic Module ◽  
Photovoltaic Modules ◽  
Input Parameters

It is difficult to determine the input parameters values for equivalent circuit models of photovoltaic modules through analytical methods. Thus, the previous researchers preferred to use numerical methods. Since, the numerical methods are time consuming and need long term time series data which is not available in most developing countries, an improved mathematical model was formulated by combination of analytical and numerical methods to overcome the limitations of existing methods. The values of required model input parameters were computed analytically. The expression for output current of photovoltaic module was determined explicitly by Lambert W function and voltage was determined numerically by Newton-Raphson method. Moreover, the algebraic equations were derived for the shape factor which involves the ideality factor and the series resistance of a single diode photovoltaic module power output model. The formulated model results were validated with rated power output of a photovoltaic module provided by manufacturers using local meteorological data, which gave ±2% error. It was found that the proposed model is more practical in terms of precise estimations of photovoltaic module power output for any required location and number of variables used.

scholarly journals Spaceborne Satellite for Snow Cover and Hydrological Characteristic of the Gilgit River Basin, Hindukush–Karakoram Mountains, Pakistan

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 531 ◽  
Author(s):  
Dostdar Hussain ◽  
Chung-Yen Kuo ◽  
Abdul Hameed ◽  
Kuo-Hsin Tseng ◽  
Bulbul Jan ◽  
...  
Keyword(s):  
Snow Cover ◽  
River Runoff ◽  
Time Series Data ◽  
Meteorological Data ◽  
Pearson Correlation ◽  
Indus Basin ◽  
Series Data ◽  
Northwestern Part ◽  
Temperature And Precipitation ◽  
Mean Temperature

The Indus River, which flows through China, India, and Pakistan, is mainly fed by melting snow and glaciers that are spread across the Hindukush–Karakoram–Himalaya Mountains. The downstream population of the Indus Plain heavily relies on this water resource for drinking, irrigation, and hydropower generation. Therefore, its river runoff variability must be properly monitored. Gilgit Basin, the northwestern part of the Upper Indus Basin, is selected for studying cryosphere dynamics and its implications on river runoff. In this study, 8-day snow products (MOD10A2) of moderate resolution imaging spectroradiometer, from 2001 to 2015 are selected to access the snow-covered area (SCA) in the catchment. A non-parametric Mann–Kendall test and Sen’s slope are calculated to assess whether a significant trend exists in the SCA time series data. Then, data from ground observatories for 1995–2013 are analyzed to demonstrate annual and seasonal signals in air temperature and precipitation. Results indicate that the annual and seasonal mean of SCA show a non-significant decreasing trend, but the autumn season shows a statistically significant decreasing SCA with a slope of −198.36 km2/year. The annual mean temperature and precipitation show an increasing trend with highest values of slope 0.05 °C/year and 14.98 mm/year, respectively. Furthermore, Pearson correlation coefficients are calculated for the hydro-meteorological data to demonstrate any possible relationship. The SCA is affirmed to have a highly negative correlation with mean temperature and runoff. Meanwhile, SCA has a very weak relation with precipitation data. The Pearson correlation coefficient between SCA and runoff is −0.82, which confirms that the Gilgit River runoff largely depends on the melting of snow cover rather than direct precipitation. The study indicates that the SCA slightly decreased for the study period, which depicts a possible impact of global warming on this mountainous region.

scholarly journals Climate Change Made Major Contributions to Soil Water Storage Decline in the Southwestern US during 2003–2014

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1947
Author(s):  
Jianzhao Liu ◽  
Liping Gao ◽  
Fenghui Yuan ◽  
Yuedong Guo ◽  
Xiaofeng Xu
Keyword(s):  
Climate Change ◽  
Soil Water ◽  
Water Resource Management ◽  
Time Series Data ◽  
Meteorological Data ◽  
Water Storage ◽  
Climate Change Impacts ◽  
Soil Water Storage ◽  
Series Data ◽  
Atmospheric Water

Soil water shortage is a critical issue for the Southwest US (SWUS), the typical arid region that has experienced severe droughts over the past decades, primarily caused by climate change. However, it is still not quantitatively understood how soil water storage in the SWUS is affected by climate change. We integrated the time-series data of water storage and evapotranspiration derived from satellite data, societal water consumption, and meteorological data to quantify soil water storage changes and their climate change impacts across the SWUS from 2003 to 2014. The water storage decline was found across the entire SWUS, with a significant reduction in 98.5% of the study area during the study period. The largest water storage decline occurred in the southeastern portion, while only a slight decline occurred in the western and southwestern portions of the SWUS. Net atmospheric water input could explain 38% of the interannual variation of water storage variation. The climate-change-induced decreases in net atmospheric water input predominately controlled the water storage decline in 60% of the SWUS (primarily in Texas, Eastern New Mexico, Eastern Arizona, and Oklahoma) and made a partial contribution in approximately 17% of the region (Central and Western SWUS). Climate change, primarily as precipitation reduction, made major contributions to the soil water storage decline in the SWUS. This study infers that water resource management must consider the climate change impacts over time and across space in the SWUS.

Mathematical modelling of ammonia volatilization from slurry stores and its effect on Cryptosporidium oocyst viability

1995 ◽  
Vol 124 (1) ◽  
pp. 55-60 ◽  
Author(s):  
G. D. Ruxton
Keyword(s):  
Mathematical Model ◽  
Time Series ◽  
Mathematical Modelling ◽  
Ammonia Volatilization ◽  
Time Series Data ◽  
Cryptosporidium Oocyst ◽  
Critical Value ◽  
Series Data ◽  
Simple Mathematical Model ◽  
Ammonia Loss

SUMMARYA simple mathematical model is shown to provide reasonably good predictions of time series data on ammonia loss from three experiments on slurry stores. The model produces predictions of changes in the distribution of ammonia with depth. Experimental evidence suggests that ammonia concentrations above a critical value appear to destroy the viability of Cryptosporidium oocysts. Using this criterion, the model predicts that, even under the most favourable circumstances, oocysts are unlikely to remain viable in slurry stores except in the top few centimetres. The effect of stirring the slurry is considered.

scholarly journals Deep learning classification of temporal data in ecology

2020 ◽  
Author(s):  
César Capinha ◽  
Ana Ceia-Hasse ◽  
Andrew M. Kramer ◽  
Christiaan Meijer
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Meteorological Data ◽  
Species Distribution Modelling ◽  
Series Data ◽  
Temporal Data ◽  
Climate Time Series ◽  
Biological Entities

AbstractTemporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach transforms the temporal data into static predictors of the classes. However, recent deep learning techniques can perform the classification using raw time series, eliminating subjective and resource-consuming data transformation steps, and potentially improving classification results. We present a general approach for time series classification that considers multiple deep learning algorithms and illustrate it with three case studies: i) insect species identification from wingbeat spectrograms; ii) species distribution modelling from climate time series and iii) the classification of phenological phases from continuous meteorological data. The deep learning approach delivered ecologically sensible and accurate classifications, proving its potential for wide applicability across subfields of ecology. We recommend deep learning as an alternative to techniques requiring the transformation of time series data.

scholarly journals Effect of temporal averaging of meteorological data on predictions of groundwater recharge

2018 ◽  
Vol 66 (2) ◽  
pp. 143-152 ◽  
Author(s):  
Marcia S. Batalha ◽  
Maria C. Barbosa ◽  
Boris Faybishenko ◽  
Martinus Th. van Genuchten
Keyword(s):  
Time Series ◽  
Groundwater Recharge ◽  
Time Series Data ◽  
Meteorological Data ◽  
Root Water Uptake ◽  
Series Data ◽  
Deep Penetration ◽  
Temporal Averaging ◽  
Recharge Rates ◽  
Groundwater Recharge Rate

AbstractAccurate estimates of infiltration and groundwater recharge are critical for many hydrologic, agricultural and environmental applications. Anticipated climate change in many regions of the world, especially in tropical areas, is expected to increase the frequency of high-intensity, short-duration precipitation events, which in turn will affect the groundwater recharge rate. Estimates of recharge are often obtained using monthly or even annually averaged meteorological time series data. In this study we employed the HYDRUS-1D software package to assess the sensitivity of groundwater recharge calculations to using meteorological time series of different temporal resolutions (i.e., hourly, daily, weekly, monthly and yearly averaged precipitation and potential evaporation rates). Calculations were applied to three sites in Brazil having different climatological conditions: a tropical savanna (the Cerrado), a humid subtropical area (the temperate southern part of Brazil), and a very wet tropical area (Amazonia). To simplify our current analysis, we did not consider any land use effects by ignoring root water uptake. Temporal averaging of meteorological data was found to lead to significant bias in predictions of groundwater recharge, with much greater estimated recharge rates in case of very uneven temporal rainfall distributions during the year involving distinct wet and dry seasons. For example, at the Cerrado site, using daily averaged data produced recharge rates of up to 9 times greater than using yearly averaged data. In all cases, an increase in the time of averaging of meteorological data led to lower estimates of groundwater recharge, especially at sites having coarse-textured soils. Our results show that temporal averaging limits the ability of simulations to predict deep penetration of moisture in response to precipitation, so that water remains in the upper part of the vadose zone subject to upward flow and evaporation.

scholarly journals PENDISC: A Simple Method for Constructing a Mathematical Model from Time-Series Data of Metabolite Concentrations

2014 ◽  
Vol 76 (6) ◽  
pp. 1333-1351 ◽  
Author(s):  
Kansuporn Sriyudthsak ◽  
Michio Iwata ◽  
Masami Yokota Hirai ◽  
Fumihide Shiraishi
Keyword(s):  
Mathematical Model ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Simple Method ◽  
Metabolite Concentrations

scholarly journals SPATIO-TEMPORAL CHANGES OF NET PRIMARY PRODUCTIVITY AND ITS RESPONSE TO PHENOLOGY IN NORTHEAST CHINA DURING 2000–2015

2018 ◽  
Vol XLII-3 ◽  
pp. 1453-1459
Author(s):  
Y. Qiu ◽  
L. Zhang ◽  
D. Fan
Keyword(s):  
Primary Productivity ◽  
Northeast China ◽  
Net Primary Productivity ◽  
Time Series Data ◽  
Meteorological Data ◽  
Geographical Location ◽  
Growing Season ◽  
Series Data ◽  
Threshold Method ◽  
The Relationship

The relationship between net primary productivity (NPP) and phenological changes is of great significance to the study of regional ecosystem processes. In this study, firstly, NPP was estimated with the remote sensing model based on the SPOT-VGT NDVI dataset (2000–2015), meteorological data and the vegetation map in Northeast China. Then, using NDVI time series data which was reconstructed by polynomial fitting, phenology was extracted with the dynamic threshold method. Finally, the relationship between NPP and phenology was analyzed. The results showed that NPP mainly increased in the cropland, grassland, forestland and shrubland; however, vegetation NPP decreased in the ecotone among cropland, grassland and forestland. Correlation analysis suggested that the relationships between NPP and phenological metrics (i.e., the start of the growing season (SOS), the end of the growing season (EOS), the length of the growing season (LOS)) were different due to geographical location. On the whole, there was a positive correlation between NPP and the LOS in the forestland, and negative in the cropland and grassland, indicating that extended LOS can promote the accumulation of forestland NPP. By analyzing the monthly NDVI data during the vigorous growth period, the increase of NPP in the grassland and cropland was mainly due to the better growth from June to August, and shortened LOS did not lead to reduce the NPP. Generally, the response of NPP to phenology in Northeast China were more complex, showing obvious difference of vegetation types and spatial variability, we need to consider topography, community structure and other factors in the further studies.

scholarly journals Simultaneous Forecasting of Meteorological Data Based on a Self-Organizing Incremental Neural Network

2018 ◽  
Vol 22 (6) ◽  
pp. 900-906 ◽  
Author(s):  
Wonjik Kim ◽  
Osamu Hasegawa ◽  
◽  
◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Atmospheric Pressure ◽  
Time Series Data ◽  
Meteorological Data ◽  
Series Data ◽  
Total Solar Radiation ◽  
Proposed Model ◽  
Self Organizing

In this study, we propose a simultaneous forecasting model for meteorological time-series data based on a self-organizing incremental neural network (SOINN). Meteorological parameters (i.e., temperature, wet bulb temperature, humidity, wind speed, atmospheric pressure, and total solar radiation on a horizontal surface) are considered as input data for the prediction of meteorological time-series information. Based on a SOINN within normalized-refined-meteorological data, proposed model succeeded forecasting temperature, humidity, wind speed and atmospheric pressure simultaneously. In addition, proposed model does not take more than 2 s in training half-year period and 15 s in testing half-year period. This paper also elucidates the SOINN and the algorithm of the learning process. The effectiveness of our model is established by comparison of our results with experimental results and with results obtained by another model. Three advantages of our model are also described. The obtained information can be effective in applications based on neural networks, and the proposed model for handling meteorological phenomena may be helpful for other studies worldwide including energy management system.

scholarly journals Metode ARIMA untuk Memodelkan Volume Produksi Kelapa Sawit pada PT. SOCFINDO di Kabupaten Aceh Tamiang

2021 ◽  
Vol 3 (2) ◽  
pp. 309-319
Author(s):  
Wiwin Apriani ◽  
◽  
Rahmi Hayati
Keyword(s):  
Mathematical Model ◽  
Time Series ◽  
Stationary State ◽  
Oil Palm ◽  
Time Series Data ◽  
Series Data ◽  
Test Results ◽  
Selection Of

This study aims to create a mathematical model that can be used to predict the amount of oil palm that will be produced at PT. Socfindo in Aceh Tamiang Regency in the coming period. The data used is data on the amount of oil palm that is ready to be produced every month in 2012-2015. The method used is the ARIMA method. The selection of this method is based on the data used, namely time series data. Before carrying out further testing, first, ensure that the data used meets the stationary state. From the test results, it is found that the data used fulfills the stationary state, then it is found that the MA (1) model can be used to predict the time series data. Furthermore, we obtain a model that can be used to predict the volume of oil palm production at PT. Socfindo is: Z_t = a_t-0.4096a_ (t-1) +521.57 With a_t ~ N (0; 29192.72)

PyEt - a Python package to estimate potential and reference evapotranspiration

2021 ◽  
Author(s):  
Matevž Vremec ◽  
Raoul Collenteur
Keyword(s):  
Agricultural Sector ◽  
Time Series Data ◽  
Hydrological Cycle ◽  
Meteorological Data ◽  
Aerodynamic Resistance ◽  
Series Data ◽  
Data Types ◽  
Empirical Formulas ◽  
3D Data ◽  
Python Package

<p>Evapotranspiration (ET) is a major component of the hydrological cycle and accurate estimates of the flux are important to the water and agricultural sector, among others. Due to difficulties in the direct observation of ET in the field, the flux is often estimated from other meteorological data using empirical formulas. There is a wide variety of such formulas, with different levels of input data and parameter requirements. While some Python packages are available in the Python ecosystem for these tasks, they typically focus on one specific formula or data type. The goal of PyEt is to provide a Python package for the estimation of ET that works with many different data types, is well documented and tested, and simple to use. The source code is hosted at GitHub (https://github.com/phydrus/PyEt) and Pypi can be used to install the package. PyEt currently contains nine different methods to estimate ET and various methods to estimate surface and aerodynamic resistance. The methods are tested against other open source data to ensure proper functioning of the methods. While the methods currently are only implemented for 1D data (e.g. time series data), future work will focus on enabling the methods on 2D and 3D data as well (such as Numpy Arrays, XArray, and NetCDF files). The package allows hydrologists to compute and compare evapotranspiration estimates using different approaches with minimum effort. The presentation will focus on the problems associated with reproducibility in ET estimation and linkage with existing Python libraries to perform complex sensitivity and uncertainty analyses.</p>

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