scholarly journals Deriving Cloud Velocity From an Array of Solar Radiation Measurements

2012 ◽  
Author(s):  
Juan L. Bosch ◽  
Yuehai Zheng ◽  
Jan Kleissl
Keyword(s):  
Solar Radiation ◽  
Test Bed ◽  
Short Term ◽  
Term Forecast ◽  
Main Variable ◽  
Input Radiation ◽  
Forecast Models ◽  
Spatio Temporal ◽  
Radiation Measurements ◽  
Good Agreement

Spatio-temporal variability of solar radiation is the main variable affecting the photovoltaic power feed-in to the grid. Clouds are the main source of such variability and their velocity is a principal input to most short-term forecast models. The main goal in this study is to estimate cloud speed using radio-metric data using measurements from 8 sensors located at the UC San Diego Solar Energy test bed. Two different methods were developed to estimate the cloud speed based on the correlation between the signals from different sensors. Our analysis showed good agreement between both methods. Additional measurements from nearby METAR and radiosonde stations also show comparable results. Both methods require high variability in the input radiation.

The analysis of short-term forecast models for oak forest damage by gypsy moth (Lymantria dispar L.).

2017 ◽  
Author(s):  
Н.И. Лямцев
Keyword(s):  
Population Density ◽  
Forest Damage ◽  
Stand Age ◽  
Short Term ◽  
Short Term Forecast ◽  
Term Forecast ◽  
Forecast Efficiency ◽  
Insect Mortality ◽  
Forecast Models ◽  
Mean Phytomass

Проведена предварительная верификация четырех моделей краткосрочного прогнозирования дефолиации дубрав непарным шелкопрядом путем их сравнительного анализа. Модели характеризуют два подхода в определении будущей дефолиации по числу зимующих яиц насекомого: а) оценка кормовой нормы (количества зеленой массы, уничтоженной средней гусеницей в условиях нормальной смертности); б) использование соотношения между потерями листвы и величиной плотности популяции. Эффективность прогнозирования зависит от точности определения количества зеленой массы дерева и насаждения и численности питающихся гусениц (варьирования смертности насекомых). В моделях использовались разные элементарные единицы учета для оценки плотности популяции (деревья разного возраста или стволы разного диаметра, побег текущего года, 100 г листвы). По установленным соотношениям между этими единицами модели были пересчитаны и приведены к единой шкале. В качестве предиктора использован возраст насаждений. Прогнозные оценки, полученные по разным моделям, относительно близки в молодняках и средневозрастных древостоях. При увеличении возраста растет и варьирование оценок. Необходимы дальнейшая верификация моделей и их корректировка, так как для производственного прогнозирования они достаточно надежны только при возрасте насаждений до 60 лет. Вероятно, требуется в большей степени учитывать и региональные различия в средней массе листьев дуба. Preliminary verification of 4 short-term forecast models with their comparative analysis is presented. The models specify two approaches to identify future defoliation by insects that hibernate as eggs: a) feeding rate assessment (foliage mass consumed by an average caterpillar in normal mortality conditions); b) application of a ratio between foliage loss and population density. Forecast efficiency depends on accuracy of tree and whole stand foliage mass assessment and feeding caterpillar population (insect mortality variation). Various units were applied in population density assessment (trees of various age or diameter, current year shoot, 100 g of foliage). The models were recalculated and reduced to a unified scale based on found ratios between these units. Forest age was used as a predictor. The forecast assessments derived in various models are relatively close for young and mid-aged stands. Variation of the assessments grows with stand age increase. Further verification and adjustment of the models is necessary since they are only reliable in forecast for stands younger than 60 years. Probably regional difference in oak foliage mean phytomass should be taken more into consideration.

scholarly journals The Seamless Solar Radiation (SESORA) Forecast for Solar Surface Irradiance—Method and Validation

2019 ◽  
Vol 11 (21) ◽  
pp. 2576 ◽  
Author(s):  
Isabel Urbich ◽  
Jörg Bendix ◽  
Richard Müller
Keyword(s):  
Solar Radiation ◽  
Solar Surface ◽  
Satellite Measurements ◽  
Short Term ◽  
Short Term Forecast ◽  
Surface Irradiance ◽  
Term Forecast ◽  
Absolute Bias ◽  
Global Irradiation ◽  
Spectrally Resolved

Due to the integration of fluctuating weather-dependent energy sources into the grid, the importance of weather and power forecasts grows constantly. This paper describes the implementation of a short-term forecast of solar surface irradiance named SESORA (seamless solar radiation). It is based on the the optical flow of effective cloud albedo and available for Germany and parts of Europe. After the clouds are shifted by applying cloud motion vectors, solar radiation is calculated with SPECMAGIC NOW (Spectrally Resolved Mesoscale Atmospheric Global Irradiance Code), which computes the global irradiation spectrally resolved from satellite imagery. Due to the high spatial and temporal resolution of satellite measurements, solar radiation can be forecasted from 15 min up to 4 h or more with a spatial resolution of 0.05 ∘ . An extensive validation of this short-term forecast is presented in this study containing two different validations based on either area or stations. The results are very promising as the mean RMSE (Root Mean Square Error) of this study equals 59 W/m 2 (absolute bias = 42 W/m 2 ) after 15 min, reaches its maximum of 142 W/m 2 (absolute bias = 97 W/m 2 ) after 165 min, and slowly decreases after that due to the setting of the sun. After a brief description of the method itself and the method of the validation the results will be presented and discussed.

scholarly journals A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo

2018 ◽  
Author(s):  
Isabel Urbich ◽  
Jörg Bendix ◽  
Richard Müller
Keyword(s):  
Solar Radiation ◽  
Optical Flow ◽  
Spatial Scales ◽  
Surface Radiation ◽  
Estimation Methods ◽  
Short Term ◽  
Short Term Forecast ◽  
Cloud Albedo ◽  
Optical Flow Method ◽  
Term Forecast

The increasing use of renewable energies as a source of electricity has led to a fundamental transition of the power supply system. The integration of fluctuating weather-dependent energy sources into the grid already has a major impact on the load flows of the grid. As a result, the interest in forecasting wind and solar radiation with a sufficient accuracy over short time horizons grew. In this study the short-term forecast of the effective cloud albedo based on optical flow estimation methods are investigated. The optical flow method utilized here is TV-L1 from the open source library OpenCV. This method uses a multi-scale-approach to capture cloud motions on various spatial scales. After the clouds are displaced the solar surface radiation will be calculated with SPECMAGIC NOW which computes the global irradiation spectrally resolved from satellite imagery. Due to a high temporal and spatial resolution of satellite measurements the effective cloud albedo and thus solar radiation can be forecasted from 5 minutes up to 4 hours with a resolution of 0.05°. In the following there will be a brief description of the method for the short-term forecast of the effective cloud albedo. Subsequently evaluation results will be presented and discussed. Finally an outlook of further developments will be given.

scholarly journals Nonlinear time-series forecasts for decision support: short-term demand for ICU beds in Santiago, Chile during the 2021 COVID-19 pandemic

2021 ◽  
Author(s):  
Bernardo F. Quiroga ◽  
Cristián Vásquez ◽  
María Ignacia Vicuña
Keyword(s):  
Intensive Care Unit ◽  
Time Series ◽  
Intensive Care ◽  
Decision Support ◽  
Health Services ◽  
Nonlinear Time Series ◽  
Metropolitan Region ◽  
Short Term ◽  
Term Forecast ◽  
Forecast Models

Abstract In Chile, due to the explosive increase of new COVID-19 cases during the first part of 2021, the ability of health services to accommodate new incoming cases was jeopardized. It has become necessary to be able to manage intensive care unit (ICU) capacity, and for this purpose, monitoring both the evolution of new cases and the demand of ICU beds, has become urgent. This paper presents short-term forecast models for the number of new cases and the number of COVID-19 patients admitted to ICUs in the Metropolitan Region in Chile.

scholarly journals Feasibility of very short-term forecast models for COVID-19 hospital-based surveillance

2021 ◽  
Vol 54 ◽  
Author(s):  
Edson Zangiacomi Martinez ◽  
Afonso Dinis Costa Passos ◽  
Antônio Fernando Cinto ◽  
Andreia Cássia Escarso ◽  
Rosane Aparecida Monteiro ◽  
...  
Keyword(s):  
Short Term ◽  
Short Term Forecast ◽  
Term Forecast ◽  
Forecast Models

Aerosol properties obtained on cloudless days through direct broadband solar radiation measurements

2020 ◽  
Author(s):  
Mohamed Zaiani ◽  
Abdanour Irbah ◽  
Djelloul Djafer ◽  
Julien Delanoe
Keyword(s):  
Solar Radiation ◽  
Processing Method ◽  
The Earth ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Using Data ◽  
Natural Aerosols ◽  
Radiation Measurements ◽  
Aerosol Properties ◽  
Good Agreement

<p>Anthropogenic and natural aerosols are important atmospheric constituents that can significantly reduce, by scattering and absorption, the solar radiation reaching the Earth’s surface. This impact depends on the aerosols properties, namely the optical thickness (τ), the exponent (α) and the coefficient (β) of Angström. These three parameters are first estimated by fitting the direct solar radiation measurements recorded on clear days with the Iqbal C model. The retrieval of τ and β using data collected in Tamanrasset, Southern Algeria, are in good agreement with those of retrieved by AERONET at the same time and location. However, α exponent comparison is not satisfactory, we have therefore developed an Artificial Neural Network method (ANN) to better estimate it. The ANN created was first learned from β and α obtained from AERONET. We then used β from the Iqbal C model with the ANN and obtain good estimate of α with R<sup>2</sup> of 60% compared to the Angstrom exponent from AERONET. We will first give in this presentation an overview of the Iqbal C model, then present the data used and the processing method, and finally discuss the main results of this study.</p>

scholarly journals ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail Transit

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Qicheng Tang ◽  
Mengning Yang ◽  
Ying Yang
Keyword(s):  
Short Term Memory ◽  
Forecast Model ◽  
Rail Transit ◽  
Short Term ◽  
Short Term Forecast ◽  
Term Forecast ◽  
Temporal Features ◽  
Accurate Forecast ◽  
Spatio Temporal ◽  
Lstm Network

The short-term forecast of rail transit is one of the most essential issues in urban intelligent transportation system (ITS). Accurate forecast result can provide support for the forewarning of flow outburst and enables passengers to make an appropriate travel plan. Therefore, it is significant to develop a more accurate forecast model. Long short-term memory (LSTM) network has been proved to be effective on data with temporal features. However, it cannot process the correlation between time and space in rail transit. As a result, a novel forecast model combining spatio-temporal features based on LSTM network (ST-LSTM) is proposed. Different from other forecast methods, ST-LSTM network uses a new method to extract spatio-temporal features from the data and combines them together as the input. Compared with other conventional models, ST-LSTM network can achieve a better performance in experiments.

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