Cell Area-Based Method for Analyzing the Coverage Capacity of Satellite Constellations

Constellation-to-ground coverage analysis is an important problem in practical satellite applications. The classical net point method is one of the most commonly used algorithms in resolving this problem, indicating that the computation efficiency significantly depends on the high-precision requirement. On this basis, an improved cell area-based method is proposed in this paper, in which a cell is used as the basic analytical unit. By calculating the accuracy area of a cell that is partly contained by the ground region or partly covered by the constellation, the accurate coverage area can be obtained accordingly. Experiments simulating different types of coverage problems are conducted, and the results reveal the correctness and high efficiency of the proposed analytical method.

Performance Enhancement of Roof-Mounted Photovoltaic System: Artificial Neural Network Optimization of Ground Coverage Ratio

Buildings in hot climate areas are responsible for high energy consumption due to high cooling load requirements which lead to high greenhouse gas emissions. In order to curtail the stress on the national grid and reduce the atmospheric emissions, it is of prime importance that buildings produce their own onsite electrical energy using renewable energy resources. Photovoltaic (PV) technology is the most favorable option to produce onsite electricity in buildings. Installation of PV modules on the roof of the buildings in hot climate areas has a twofold advantage of acting as a shading device for the roof to reduce the cooling energy requirement of the building while producing electricity. A high ground coverage ratio provides more shading, but it decreases the efficiency of the PV system because of self-shading of the PV modules. The aim of this paper was to determine the optimal value of the ground coverage ratio which gives maximum overall performance of the roof-mounted PV system by considering roof surface shading and self-shading of the parallel PV modules. An unsupervised artificial neural network approach was implemented for Net levelized cost of energy (Net-LCOE) optimization. The gradient decent learning rule was used to optimize the network connection weights and the optimal ground coverage ratio was obtained. The proposed optimized roof-mounted PV system was shown to have many distinct performance advantages over a typical ground-mounted PV configuration such as 2.9% better capacity factor, 15.9% more energy yield, 40% high performance ratio, 14.4% less LCOE, and 18.6% shorter payback period. The research work validates that a roof-mounted PV system in a hot climate area is a very useful option to meet the energy demand of buildings.

Predicting fertilizer treatment of maize using decision tree algorithm

<span>Machine learning approaches are progressively successful in image based analysis such as different diseases prediction as well as level of risk assessment etc. In this paper, image based data analysis with machine learning technique were used for fertilizer treatment of maize. We address this issue as our country depend on agricultural field rather than others. Maize has a bright future. To predict fertilizer treatment of maize dataset were comprised of ground coverage region which highlights the green pixels of a maize image. For calculating green pixels from an image we used “Can Eye” tool. The achievement of machine learning approaches is highly dependent on quality and quantity of the dataset which is used for training the machine for better classification result. For this perseverance, we collected images from the maize field directly. Then processed those images and classified the data into four classes (Less Nitrogen=-N, Less Phosphorus=-P, Less Potassium=-K and NPK) to train our machine using decision tree algorithm to predict fertilizer treatment. We got 93% classification accuracy for decision tree. Finally, the outcome of this paper is the fertilizer treatment of a maize field based on the ground cover percentage, and we implemented this whole work using an android platform because of the availability of android mobile phone throughout the world.</span>

Outdoor Ventilation and Ground Coverage: Exploring a Climate Centric Approach to Building Byelaws for Multi Storied Apartments in Bhubaneswar

Bhubaneswar with a warm and humid climate and with humidity much higher than the comfort level requires an enhanced natural ventilation to achieve long term quality of life. The building code which regulates the fabric of the city at present follows a standardized set of regulations governed by National Building Code of India and is developed without giving much consideration to climate. Ground coverage is an important parameter which regulates the footprint of the blocks and allows natural ventilation to buildings as well to outdoor. At present, Bhubaneswar does not prescribe a ground coverage for its apartments and completely dependent on FAR control. As a result, the developments consider quite high ground coverage in certain areas. This particular research focusses on analyzing the current situation of multi storied apartments and proposes a few climate centric recommendations for the byelaw. To examine the situation and arrive at a strategy, a simulation study has been carried out by altering the ground coverage and building orientation of a multistoried apartment consisting of 5 residential blocks to analyze the effect of natural ventilation. The study inferred that, building layout and orientation in relation to wind direction plays an important role for natural ventilation in the outdoors. A climate centric byelaw ideally should consider both while formulating its building code.

Techno-economic assessment of grid-connected photovoltaic systems in Swedish public buildings

Sweden aims to achieve near-zero non-renewable energy use in all the newly constructed buildings from 2020. One of the most promising methods of achieving these energy goals and reducing the net energy-use is using solar photovoltaic (PV) systems in buildings. Although some studies have demonstrated this method, the solar PV industry is growing rapidly. Therefore, the study aimed at using sources with the latest information to analyse the true potential of PV systems for the current initial cost of the PV system and tax benefits in Sweden. The study investigates the economic feasibility of a grid-connected solar PV system from a technical and economic perspective for a group of public buildings in Sweden. The hourly energy production and cost of purchasing deficit electricity was simulated for various tilts and ground coverage area to find the optimum tilt and ground coverage ratio of PV panels. The PV energy supply of four different systems – 26 kWp, 75 kWp, 80 kWp, 155 kWp – in different locations was simulated. The overproduction, own usage rate, solar fraction, investment cost, profit over its lifespan and the payback period of each system were compared for the existing as well as improved energy use. Honeybee 0.0.64 and SAM 2018.11.11 was used to simulate energy use and PV production. Results indicate that a system with a high own usage rate and specific yield was profitable when the selling price of electricity (excluding tax refund) was lower. However, a system with a higher production potential became more profitable when the selling price of electricity (including tax refund) was equal or higher than the purchasing price. Additionally, a sensitivity analysis was conducted to demonstrate the feasibility of the system if the price of electricity or interest rates changed in the future. The outcome of this research demonstrates the techno-economic feasibility of implementing a solar PV system in Sweden and provides a set of benchmarks for comparison of such systems around the world.

DATA-DRIVEN REAL-TIME ANALYSIS SERVICE FOR SATELLITE AREA COVERAGE

Satellite area coverage analysis is complex, especially when the satellite operating state changes. Traditional algorithms cannot quickly and efficiently obtain the satellite area coverage analysis results, and are incapable of providing efficient online services. To overcome these shortcomings and meet the needs of 21st century geospatial science and applications for real-time response and online services for handling high concurrent requests, a data-driven real-time analysis service for satellite area coverage is proposed. Firstly, to optimize traditional algorithms, an extended bounding rectangle of the ground area is constructed, and the spatial relationship with the central point of satellite ground coverage area is determined to avoid large number of calculations of satellite ground coverage areas, thus, improving efficiency. Secondly, the data-driven real-time analysis model is constructed, and is used to further improve the efficiency of satellite area coverage analysis. Finally, a simulation analysis scenario is developed, the service proposed in this study is verified, and the results are visualized. The experimental results show that the proposed data-driven real-time analysis service can cope well with the requests from users for online service of satellite area coverage, improve the cognitive capability of users and operators, and help in completely utilizing the application performance of observation satellites.