scholarly journals Short-term favorable weather conditions are an important control of interannual variability in carbon and water fluxes

2016 ◽  
Vol 121 (8) ◽  
pp. 2186-2198 ◽  
Author(s):  
Jakob Zscheischler ◽  
Simone Fatichi ◽  
Sebastian Wolf ◽  
Peter D. Blanken ◽  
Gil Bohrer ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Weather Conditions ◽  
Water Fluxes ◽  
Short Term ◽  
Favorable Weather

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

Short-term scheduling problem in smart grid considering reliability improvement in bad weather conditions

2017 ◽  
Vol 11 (10) ◽  
pp. 2521-2533 ◽  
Author(s):  
Babak Yousefi-khangah ◽  
Saeid Ghassemzadeh ◽  
Seyed Hossein Hosseini ◽  
Behnam Mohammadi-Ivatloo
Keyword(s):  
Smart Grid ◽  
Weather Conditions ◽  
Scheduling Problem ◽  
Short Term ◽  
Reliability Improvement ◽  
Bad Weather

The role of short-term weather conditions in temporal dynamics of fire regime features in mainland Spain

2019 ◽  
Vol 241 ◽  
pp. 575-586 ◽  
Author(s):  
Adrián Jiménez-Ruano ◽  
Marcos Rodrigues Mimbrero ◽  
W. Matt Jolly ◽  
Juan de la Riva Fernández
Keyword(s):  
Temporal Dynamics ◽  
Fire Regime ◽  
Weather Conditions ◽  
Short Term

scholarly journals Models and applications for risk assessment and prediction of Asian soybean rust epidemics

2006 ◽  
Vol 31 (6) ◽  
pp. 533-544 ◽  
Author(s):  
Emerson M. Del Ponte ◽  
Cláudia V. Godoy ◽  
Marcelo G. Canteri ◽  
Erlei M. Reis ◽  
X.B. Yang
Keyword(s):  
Disease Modeling ◽  
Weather Conditions ◽  
Epidemiological Studies ◽  
Soybean Rust ◽  
Phakopsora Pachyrhizi ◽  
Spatial And Temporal Scales ◽  
Asian Soybean Rust ◽  
New Development ◽  
Glycine Max L ◽  
Favorable Weather

Asian rust of soybean [Glycine max (L.) Merril] is one of the most important fungal diseases of this crop worldwide. The recent introduction of Phakopsora pachyrhizi Syd. & P. Syd in the Americas represents a major threat to soybean production in the main growing regions, and significant losses have already been reported. P. pachyrhizi is extremely aggressive under favorable weather conditions, causing rapid plant defoliation. Epidemiological studies, under both controlled and natural environmental conditions, have been done for several decades with the aim of elucidating factors that affect the disease cycle as a basis for disease modeling. The recent spread of Asian soybean rust to major production regions in the world has promoted new development, testing and application of mathematical models to assess the risk and predict the disease. These efforts have included the integration of new data, epidemiological knowledge, statistical methods, and advances in computer simulation to develop models and systems with different spatial and temporal scales, objectives and audience. In this review, we present a comprehensive discussion on the models and systems that have been tested to predict and assess the risk of Asian soybean rust. Limitations, uncertainties and challenges for modelers are also discussed.

scholarly journals Influence of hydrothermal conditions on the productivity of perennial grasses in South Steppe of Ukraine

2019 ◽  
pp. 27-36 ◽  
Author(s):  
V. F. Petrychenko ◽  
L. K. Antypova ◽  
N. V. Tsurkan
Keyword(s):  
Weather Conditions ◽  
Bromus Inermis ◽  
Perennial Grasses ◽  
Hydrothermal Conditions ◽  
Green Mass ◽  
Melilotus Albus ◽  
Perennial Legume ◽  
Favorable Weather ◽  
Perennial Legumes ◽  
Similar Yield

The purpose is to determine the productivity of perennial legume and cereal grasses under conditions of natural moisture supply in South Steppe of Ukraine. Method. The studies were conducted during 2016—2018 using conventional methods, and the output of feed units, digestible protein per unit of area was determined by reference books. Results. On average over three years of research, the highest yield of leaf-stem mass of cereals was formed by Bromus inermis and Elytrigia medium tender – 11.6 and 11.2 t/ha, respectively. The lowest yield was formed by Agropyrum pectiniforme – 7.6 t/ha. Among the all legume grasses, Melilotus albus prevailed (14.8 t/ha). Medicago sativa and Onobrychis arenaria were able to form a similar yield (14.5 and 13.5 t/ha, respectively) under the arid conditions of South Steppe of Ukraine. Insufficient rainfall in 2017 caused a decrease in the productivity of the studied crops. Thus, in 2017 the average yield of green mass in the experiment was 10.3 t/ha, while in 2016 under more favorable weather conditions this figure was 13.2 t/ha or 28.2 % more. The highest output of feed and protein units (FPU) per unit of area under cereal grasses was provided by Bromus inermis (2.35 t/ha). The lowest one was recorded in Agropyrum pectiniforme (1.60 t/ha). FPU output per unit of area under legume grasses increased respectively. Lotus corniculatus provides less green mass and therefore dry matter and forage and protein units. Perennial cereals grasses do not prevail over legume grasses, so they do not spread in South Ukraine. Conclusions. The productivity of perennial grasses in the south of Ukraine significantly depends on the type of plants, weather (hydrothermal) conditions of the year. The most effective is the cultivation of perennial legumes, namely Melilotus albus, alfalfa, Onobrychis arenaria. Bromus inermis and Elytrigia medium prevail among cereal grasses.

Comparison of short-term solar irradiance forecasting methods when weather conditions are complicated

2018 ◽  
Vol 10 (5) ◽  
pp. 053501 ◽  
Author(s):  
Yunjun Yu ◽  
Junfei Cao ◽  
Xiaofeng Wan ◽  
Fanpeng Zeng ◽  
Jianbo Xin ◽  
...  
Keyword(s):  
Solar Irradiance ◽  
Weather Conditions ◽  
Short Term ◽  
Forecasting Methods

Studying Driving Behavior on Horizontal Curves using Naturalistic Driving Study Data

2018 ◽  
Vol 2672 (17) ◽  
pp. 83-95 ◽  
Author(s):  
Bashar Dhahir ◽  
Yasser Hassan
Keyword(s):  
Data Collection ◽  
Weather Conditions ◽  
Driving Behavior ◽  
Study Data ◽  
Output Analysis ◽  
Horizontal Curves ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Design Consistency ◽  
Favorable Weather

Many studies have been conducted to develop models to predict speed and driver comfort thresholds on horizontal curves, and to evaluate design consistency. The approaches used to develop these models differ from one another in data collection, data processing, assumptions, and analysis. However, some issues might be associated with the data collection that can affect the reliability of collected data and developed models. In addition, analysis of speed behavior on the assumption that vehicles traverse horizontal curves at a constant speed is far from actual driving behavior. Using the Naturalistic Driving Study (NDS) database can help overcome problems associated with data collection. This paper aimed at using NDS data to investigate driving behavior on horizontal curves in terms of speed, longitudinal acceleration, and comfort threshold. The NDS data were valuable in providing clear insight on drivers’ behavior during daytime and favorable weather conditions. A methodology was developed to evaluate driver behavior and was coded in Matlab. Sensitivity analysis was performed to recommend values for the parameters that can affect the output. Analysis of the drivers’ speed behavior and comfort threshold highlighted several issues that describe how drivers traverse horizontal curves that need to be considered in horizontal curve design and consistency evaluation.

Near Optimal Receding Horizon Control of Thermal Energy Storage

2021 ◽  
pp. 1-13
Author(s):  
Omer Ahmed Qureshi ◽  
Peter R. Armstrong
Keyword(s):  
Energy Storage ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Energy Savings ◽  
Search Algorithm ◽  
Cost Savings ◽  
Weather Conditions ◽  
Receding Horizon Control ◽  
Receding Horizon ◽  
Favorable Weather

Abstract Efficient plant operation can be achieved by properly loading and sequencing available chillers to charge and discharge a thermal energy storage (TES) reservoir at optimal rates and times. TES charging sequences are often determined by heuristic rules that typically aim to reduce utility costs under time of use rates. However, such rules of thumb may result in significantly sub optimal performance on somedays. Rigorous optimization, on the other hand, is computationally expensive and can be unreliable as well if not carefully implemented. Receding Horizon Control (RHC) using the novel finite search algorithm is reliable and can reach ~80% of achievable energy efficiency and/or peak shifting capacity has been our target. A novel algorithm is developed to reliably achieve near optimal control for charging the stratified sensible cool storage reservoir of a chiller plant. The algorithm provides a constant COP (or cost per ton-hour) for 24-hr dispatch plan under which chillers operate during most favorable weather conditions. Analysis of four hot climates, ranging from humid to dry, indicates 2.4~2.6% energy savings under a flat electricity rate relative to the same plant operating without TES. Annual cost savings from 6% to 9% was found for electricity billed under a simple (10am-10pm) time-of-use rate with no demand charge and no ratchet component.

Impacts of interannual climate and biophysical variability on global agriculture markets

2020 ◽  
Author(s):  
Xin Zhao ◽  
Katherine Calvin ◽  
Marshall Wise ◽  
Pralit Patel ◽  
Abigail Snyder ◽  
...  
Keyword(s):  
Land Use ◽  
Interannual Variability ◽  
Weather Conditions ◽  
Climate Impacts ◽  
Interannual Variations ◽  
Standard Assumption ◽  
Adaptive Expectations ◽  
Equilibrium Modeling ◽  
Production And Consumption ◽  
Global Agriculture

Abstract Most studies assessing climate impacts on agriculture have focused on average changes in market-mediated responses (e.g., changes in land use, production, and consumption). However, the response of global agricultural markets to interannual variability in climate and biophysical shocks is poorly understood and not well represented in global economic models. Here we show a strong transmission of interannual variations in climate-induced biophysical yield shocks to agriculture markets, which is further magnified by endogenous market fluctuations generated due to producers’ imperfect expectations of market and weather conditions. We demonstrate that the volatility of crop prices and consumption could be significantly underestimated (i.e., on average by 55% and 41%, respectively) by assuming perfect foresight, a standard assumption in the economic equilibrium modeling, compared with the relatively more realistic adaptive expectations. We also find heterogeneity in interannual variability across crops and regions, which is considerably mediated by international trade.

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