Real-Time Footfall Prediction Using Weather Data: A Case on Retail Analytics

2019 ◽  
pp. 529-542
Author(s):  
Garima Makkar
Keyword(s):  
Real Time ◽  
Weather Data ◽  
Retail Analytics

scholarly journals Development of an Online Tool for Tracking Soil Nitrogen to Improve the Environmental Performance of Maize Production

2021 ◽  
Vol 13 (10) ◽  
pp. 5649
Author(s):  
Giovani Preza-Fontes ◽  
Junming Wang ◽  
Muhammad Umar ◽  
Meilan Qi ◽  
Kamaljit Banger ◽  
...  
Keyword(s):  
Real Time ◽  
N Uptake ◽  
Growing Season ◽  
Weather Data ◽  
N Availability ◽  
Soil N ◽  
N Losses ◽  
Online Tool ◽  
Support Tool ◽  
Soil N Availability

Freshwater nitrogen (N) pollution is a significant sustainability concern in agriculture. In the U.S. Midwest, large precipitation events during winter and spring are a major driver of N losses. Uncertainty about the fate of applied N early in the growing season can prompt farmers to make additional N applications, increasing the risk of environmental N losses. New tools are needed to provide real-time estimates of soil inorganic N status for corn (Zea mays L.) production, especially considering projected increases in precipitation and N losses due to climate change. In this study, we describe the initial stages of developing an online tool for tracking soil N, which included, (i) implementing a network of field trials to monitor changes in soil N concentration during the winter and early growing season, (ii) calibrating and validating a process-based model for soil and crop N cycling, and (iii) developing a user-friendly and publicly available online decision support tool that could potentially assist N fertilizer management. The online tool can estimate real-time soil N availability by simulating corn growth, crop N uptake, soil organic matter mineralization, and N losses from assimilated soil data (from USDA gSSURGO soil database), hourly weather data (from National Weather Service Real-Time Mesoscale Analysis), and user-entered crop management information that is readily available for farmers. The assimilated data have a resolution of 2.5 km. Given limitations in prediction accuracy, however, we acknowledge that further work is needed to improve model performance, which is also critical for enabling adoption by potential users, such as agricultural producers, fertilizer industry, and researchers. We discuss the strengths and limitations of attempting to provide rapid and cost-effective estimates of soil N availability to support in-season N management decisions, specifically related to the need for supplemental N application. If barriers to adoption are overcome to facilitate broader use by farmers, such tools could balance the need for ensuring sufficient soil N supply while decreasing the risk of N losses, and helping increase N use efficiency, reduce pollution, and increase profits.

scholarly journals Forecasted Weather based Weather Data File Generation Techniques for Real-time Building Simulation

2014 ◽  
Vol 34 (1) ◽  
pp. 8-18
Author(s):  
Young-Hoon Kwak ◽  
Yong-Woo Jeong ◽  
Hey-Sim Han ◽  
Cheol-Yong Jang ◽  
Jung-Ho Huh
Keyword(s):  
Real Time ◽  
Data File ◽  
Building Simulation ◽  
Weather Data

scholarly journals An IoT based Low-cost Weather Monitoring System for Smart Farming

2021 ◽  
Author(s):  
L.P.S.S.K. Dayananda ◽  
A. Narmilan ◽  
P. Pirapuraj
Keyword(s):  
Liquid Crystal ◽  
Mobile Phone ◽  
Real Time ◽  
Monitoring System ◽  
Liquid Crystal Display ◽  
Low Cost ◽  
Weather Conditions ◽  
Weather Data ◽  
The Internet ◽  
Arduino Uno

Background: Weather monitoring is an important aspect of crop cultivation for reducing economic loss while increasing productivity. Weather is the combination of current meteorological components, such as temperature, wind direction and speed, amount and kind of precipitation, sunshine hours and so on. The weather defines a time span ranging from a few hours to several days. The periodic or continuous surveillance or the analysis of the status of the atmosphere and the climate, including parameters such as temperature, moisture, wind velocity and barometric pressure, is known as weather monitoring. Because of the increased usage of the internet, weather monitoring has been upgraded to smart weather monitoring. The Internet of Things (IoT) is one of the new technology that can help with many precision farming operations. Smart weather monitoring is one of the precision agriculture technologies that use sensors to monitor correct weather. The main objective of the research is to design a smart weather monitoring and real-time alert system to overcome the issue of monitoring weather conditions in agricultural farms in order for farmers to make better decisions. Methods: Different sensors were used in this study to detect temperature and humidity, pressure, rain, light intensity, CO2 level, wind speed and direction in an agricultural farm and real time clock sensor was used to measured real time weather data. The major component of this system was an Arduino Uno microcontroller and the system ran according to a program written in the Arduino Uno software. Result: This is a low-cost smart weather monitoring system. This system’s output unit were a liquid crystal display and a GSM900A module. The weather data was displayed on a liquid crystal display and the GSM900A module was used to send the data to a mobile phone. This smart weather station was used to monitor real-time weather conditions while sending weather information to the farmer’s mobile phone, allowing him to make better decisions to increase yield.

scholarly journals Near Real-Time Biophysical Rice (Oryza sativa L.) Yield Estimation to Support Crop Insurance Implementation in India

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1674
Author(s):  
Ponraj Arumugam ◽  
Abel Chemura ◽  
Bernhard Schauberger ◽  
Christoph Gornott
Keyword(s):  
Oryza Sativa ◽  
Real Time ◽  
Spatial Resolution ◽  
Yield Loss ◽  
Smallholder Farmers ◽  
Oryza Sativa L ◽  
Crop Insurance ◽  
Weather Data ◽  
Insurance Scheme ◽  
Agricultural Income

Immediate yield loss information is required to trigger crop insurance payouts, which are important to secure agricultural income stability for millions of smallholder farmers. Techniques for monitoring crop growth in real-time and at 5 km spatial resolution may also aid in designing price interventions or storage strategies for domestic production. In India, the current government-backed PMFBY (Pradhan Mantri Fasal Bima Yojana) insurance scheme is seeking such technologies to enable cost-efficient insurance premiums for Indian farmers. In this study, we used the Decision Support System for Agrotechnology Transfer (DSSAT) to estimate yield and yield anomalies at 5 km spatial resolution for Kharif rice (Oryza sativa L.) over India between 2001 and 2017. We calibrated the model using publicly available data: namely, gridded weather data, nutrient applications, sowing dates, crop mask, irrigation information, and genetic coefficients of staple varieties. The model performance over the model calibration years (2001–2015) was exceptionally good, with 13 of 15 years achieving more than 0.7 correlation coefficient (r), and more than half of the years with above 0.75 correlation with observed yields. Around 52% (67%) of the districts obtained a relative Root Mean Square Error (rRMSE) of less than 20% (25%) after calibration in the major rice-growing districts (>25% area under cultivation). An out-of-sample validation of the calibrated model in Kharif seasons 2016 and 2017 resulted in differences between state-wise observed and simulated yield anomalies from –16% to 20%. Overall, the good ability of the model in the simulations of rice yield indicates that the model is applicable in selected states of India, and its outputs are useful as a yield loss assessment index for the crop insurance scheme PMFBY.

Evaluating the Impact of Free-Stream Turbulence on Convective Cooling of Overhead Conductors Using Large Eddy Simulations

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
Mohamed Abdelhady ◽  
David H. Wood
Keyword(s):  
Wind Speed ◽  
Real Time ◽  
Turbulence Intensity ◽  
Free Stream ◽  
Diameter Ratio ◽  
Weather Data ◽  
Length Scale ◽  
Free Stream Turbulence ◽  
Large Eddy ◽  
The Impact

The international trend of using renewable energy sources for generating electricity is increasing, partly through harvesting energy from wind turbines. Increasing electric power transmission efficiency is achievable through using real-time weather data for power line rating, known as real-time thermal rating (RTTR), instead of using the worst case scenario weather data, known as static rating. RTTR is particularly important for wind turbine connections to the grid, as wind power output and overhead conductor rating both increase with increasing wind speed, which should significantly increase real-time rated conductor from that of statically rated. Part of the real-time weather data is the effect of free-stream turbulence, which is not considered by the commonly used overhead conductor codes, Institute of Electrical and Electronics Engineers (IEEE) 738 and International Council on Large Electric Systems (CIGRÉ) 207. This study aims to assess the effect free-stream turbulence on IEEE 738 and CIGRÉ 207 forced cooling term. The study uses large eddy simulation (LES) in the ANSYS fluent software. The analysis is done for low wind speed, corresponding to Reynolds number of 3000. The primary goal is to calculate Nusselt number for cylindrical conductors with free-stream turbulence. Calculations showed an increase in convective heat transfer from the low turbulence value by ∼30% at turbulence intensity of 21% and length scale to diameter ratio of 0.4; an increase of ∼19% at turbulence intensity of 8% and length scale to diameter ratio of 0.4; and an increase of ∼15% at turbulence intensity of 6% and length scale to diameter ratio of 0.6.

Continuous Monitoring, Modeling, and Evaluation of Actual Building Energy Systems

2014 ◽  
Author(s):  
Maxim L. Sankey ◽  
Sheldon M. Jeter ◽  
Trevor D. Wolf ◽  
Donald P. Alexander ◽  
Gregory M. Spiro ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Continuous Monitoring ◽  
Energy System ◽  
Building Energy ◽  
Energy Performance ◽  
Weather Data ◽  
Practical Procedure ◽  
Heating And Cooling ◽  
Institute Of Technology

Residential and commercial buildings account for more than 40% of U.S. energy consumption, most of which is related to heating, ventilation and air conditioning (HVAC). Consequently, energy conservation is important to building owners and to the economy generally. In this paper we describe a process under development to continuously evaluate a building’s heating and cooling energy performance in near real-time with a procedure we call Continuous Monitoring, Modeling, and Evaluation (CMME). The concept of CMME is to model the expected operation of a building energy system with actual weather and internal load data and then compare modeled energy consumption with actual energy consumption. For this paper we modeled two buildings on the Georgia Institute of Technology campus. After creating our building models, internal lighting loads and equipment plug-loads were collected through electrical sub-metering, while the building occupancy load was recorded using doorway mounted people counters. We also collected on site weather and solar radiation data. All internal loads were input into the models and simulated with the actual weather data. We evaluated the building’s overall performance by comparing the modeled heating and cooling energy consumption with the building’s actual heating and cooling energy consumption. Our results demonstrated generally acceptable energy performance for both buildings; nevertheless, certain specific energy inefficiencies were discovered and corrective actions are being taken. This experience shows that CMME is a practical procedure for improving the performance of actual well performing buildings. With improved techniques, we believe the CMME procedure could be fully automated and notify building owners in real-time of sub-optimal building performance.

scholarly journals Automatic processing, quality assurance and serving of real-time weather data

2011 ◽  
Vol 37 (3) ◽  
pp. 353-362 ◽  
Author(s):  
Matthew Williams ◽  
Dan Cornford ◽  
Lucy Bastin ◽  
Richard Jones ◽  
Stephen Parker
Keyword(s):  
Quality Assurance ◽  
Real Time ◽  
Automatic Processing ◽  
Weather Data ◽  
Processing Quality
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