Solar Panel Estimator User Guide

Transcription

1 Solar Panel Estimator User Guide Hiu Hong Yu, Wen Xi Zhang, Terence Wu, Raymond Christy Version 1.3, 5/26/2015 A free open- source Solar Panel Estimator. This documentation is distributed by the Team Excalihacker of the University of California Davis under the Creative Commons Attribution- NonCommercial- ShareAlike 4.0 International License Team Exclihacker, University of California - Davis, One Shields Avenue, Davis, CA 95616

2 Solar Panel Estimator User Guide Solar Panel Estimator User Guide Version 1.3, 5/26/2015 By Hiu Hong Yu, Wen Xi Zhang, Terence Wu, Raymond Christy This documentation is distributed by the Team Excalihacker of the University of California Davis under the Creative Commons Attribution- NonCommercial- ShareAlike 4.0 International License 2

3 Table of Contents Table of Contents Introduction Open- Source Dependency Python- Flask Introduction Latest Release Download More Information OpenCV Introduction Latest Release Download More Information Google Geocoding API Introduction API Key More Information Google API Key Create a Google account Obtain Google API Key Enabling Billing Solar Panel Estimator Introduction Condition for Potential Area Results Limitation System Requirement Minimum Requirement Recommended Requirement Windows Installation/Configuration Step Step Step Step Step Step OSX Installation/Configuration Step Step Step

4 Step Step Step Linux Installation/Configuration Ubuntu Step Step Step Execution Step Step Step Future Development Reference Contact Us

5 1. Introduction While most of the electricity we use today is generated using fossil fuels, these non- renewable energy sources are starting to raise a number of serious concerns such as price increase, pollution, and depletion. As a result, people and businesses are starting to look for alternative energy resources. Solar energy has been considered to be the next favorable source of energy because of the advantages it can offer to individuals, businesses and communities such as reduction in utility costs, easy installations, increase in property value, and other benefits. The advancement in solar technologies along with government incentives has made solar energy more affordable. As solar energy becomes more popular, it is important for city planners to understand the impact of solar energy in the community. Currently, in order to calculate the amount of solar power that can be produced by a house or building, technicians are needed to manually perform calculations of the roofs using satellite images or, in some cases, visit the location in person. This is unquestionably time- consuming for city planners to calculate the amount of energy that can be generated by a whole region. Therefore, Gerry Braun, the director of the Integrated Resources Network, proposed an idea to create software to automate this process. This software will use GIS information and satellite images to approximate the amount of solar power that could potentially be generated from an area. This will serve as a powerful tool for city planners and researchers to have a grasp of how much impact will solar powers have on the community. 5

6 2. Open- Source Dependency Solar Panel Estimator required the following open- source libraries: OpenCV Python 2.7 g++ libcurl4- openssl- dev Python- flask 6

7 3. Python- Flask 3.1 Introduction Flask is a microframework for Python based on Werkzeug, Jinja 2 and good intentions. And before you ask: It's BSD licensed! 3.1 Latest Release For more information on Python- Flask s latest release, please see Python- Flask News for details. 3.2 Download For downloading the latest release source, please see Python- Flask Download for details. 3.3 More Information For more information and F&Q, please see Python- Flask home page for details. 7

8 4. OpenCV 4.1 Introduction OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD- licensed product, OpenCV makes it easy for businesses to utilize and modify the code. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state- of- the- art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 7 million. The library is used extensively in companies, research groups and by governmental bodies. 4.2 Latest Release For more information on OpenCV s latest release, please see OpenCV News for details. 4.3 Download For downloading the latest release source, please see OpenCV Download for details. 4.4 More Information For more information and F&Q, please see OpenCV home page for details. 8

9 5. Google Geocoding API 5.1 Introduction Geocoding is the process of converting addresses (like "1600 Amphitheatre Parkway, Mountain View, CA") into geographic coordinates (like latitude and longitude ), which you can use to place markers on a map, or position the map. Reverse geocoding is the process of converting geographic coordinates into a human- readable address. The Geocoding API's reverse geocoding service also lets you find the address for a given place ID. The Google Geocoding API provides a direct way to access these services via an HTTP request. 5.2 API Key In order to have access to Google Geocoding API, you will need to obtain a Google API Key. See Chapter 6 for step- by- steps Key Obtain process. 5.3 More Information For more information and F&Q, see Google Geocoding API for details. 9

10 6. Google API Key The follow step- by- steps guide will guide you through getting the API key setup. 6.1 Create a Google account You may skip this step if you already have a Google Account. To create a Google account, simply click on Create Google Account and follow the instruction on the website. 6.2 Obtain Google API Key You may skip this step if you already have a Google API Key. After setting up a Google Account, navigate to Google Developer Console to enable Geocoding and Map services by creating a new Project. 10

11 Click on Create Project: Fill in the Project name section and leave everything else as is. Then click on Create. When then project is created successfully: Click on the project name, and you will be redirect to Project Dashboard. 11

12 Then Click on APIs & auth to expand the manual on the left under Overview, and click on APIs Enable Static Map API, Geocoding API and Google Maps Geolocation API by clicking on each one and enable them. Enable each API by clicking on Enable API button. 12

13 After Enabling all necessary APIs, click on Credentials on the left manual (boxed in Red). Click on Create new key (boxed in Blue) if it stated No keys found (boxed in Orange) under Public API access to create an API key. Select Server key 13

14 Click Create If all steps gone smoothly, you should see the following screen, where the API key is located inside the orange box. 6.3 Enabling Billing Each API is limited by the free quota shown in Quota section as shown below. To receive high quota, enable the billing services by the follow steps: Navigate to the API you wish to enable billing, click on Enable billing under Quota. Follow the instruction to enable billing to receive high quota. 14

15 7. Solar Panel Estimator 7.1 Introduction Condition for Potential Area There are conditions for determining whether a certain area is suitable for solar panels. Conditions to determine an area is suitable for solar panel are: o If there are no shadow detected in the satellite image. o If a certain area has obstacles in the way that prevent panel installation. These obstacles include chimneys, edges and joining of different side of the roofs. 7.2 Results When all calculation is completed, all results files will be output in a timestamp folder under the location where user provided to the setup script. See Chapter 8.2 for setup details. Results file including: CSV - solar panel information for each house within the region selected. CSV (Optional) - solar panel information for each house within the region selected verified by Google API. CSV Images file name with it corresponding latitude and longitude location. Image(s) 1920x1920 pixels of satellite image(s) with potential and estimated solar panel area shown on the rooftop of each house. 7.3 Limitation Usage of the Google API Key. Google only allow 2500 requests per day for free users and 100,000 requests per day for paid users. Each block for calculation requires two images to be downloaded. Querying the correct address for each house requires 2 queries. See Google API for details. The accuracy of solar estimator completely depends on the images Google Map provides. However, Google Map s satellite view and street view does not completely match with each other, which causes some small amount of error. There is no way to detect the slope of roofs from satellite image, which result in detected area to be smaller than actual roof area. Because we download our image with a fixed size, the region users select will not be exact region that will be calculated. The orientation of calculated solar panel location might not be ideal for large buildings. Since images are processed in individual pieces, some houses might be cut off, resulting in estimation error on building on the edge of images. This error decreases by stitching adjacent images together before processing, resulting in higher memory usage. 15

16 8. System Requirement 8.1 Minimum Requirement Operating System: Ubuntu 12.04LTS/Window 7 x64/osx System Memory: 4GB Graphic Card: N/A Storage: 80GB 8.2 Recommended Requirement Operating System: Ubuntu 14.04LTS/Window 7 x64/osx System Memory: 8 16 GB Graphic Card: N/A Storage: 160GB 16

17 9. Windows Installation/Configuration All Installation files and Virtual Image are located under the SPE Solid State Drive (SSD). To ensure program stability, Solar Panel Estimator (SPE) will run under Virtual Machine on Window System. See Chapter 8.1 for System Requirement. Step 1 To setup SPE on Windows, first connect the SPE SSD on Windows machine. After all USB Bus drivers are installed, you will see the screen shown below. Select Open folder to view files. Navigate to SolarPanelEstimator folder and copy the SolarPanelEstimator_v1.3 folder to the locate machine. Please remember the location, as we will need it later. Step 2 Next Navigate to Windows folder 17

18 Double click on VirtualBox Win.exe Follow the instruction from VirtualBox installer to complete installation. Keep all setting as is. 18

19 Click Install to install the VirtualBox USB Driver. To avoid additional Windows Security, you may check Always trust software from Oracle Corporation before clicking Install and Skip to Import VirtualBox Image. Click Install to install VirtualBox Network Driver Click Install to install VirtualBox Chipset Driver 19

20 Step 3 When installation is completed, run VirtualBox and Import SPE Image by: File à Import Appliance Select the SolarPanelEstimator_v1.3 Image that you copied during the first step and click Next. 20

21 Step 4 After Importing the SolarPanelEstimator_v1.3, you should see the screen shown below. Before starting the Virtual Machine (VM), click on Settings à General à Advance, Change Drag n Drop to Bidirectional 21

22 Next, setup shared folder between Host and Guest by navigating to Settings à Shared Folders à Add new folder (folder with the + icon) Select the Folder you wish to share with the Guest (VM). This folder will use to store any output from Shared Folder name, Please remember this name, as we will use it in the next Section. Check Auto- mount SolaPanelEstimator. Step 5 When all configurations are completed, you are ready to start the virtual Machine. After VM started up, open up Terminal by pressing Ctrl + Alt + T and execute the following command on Desktop. sh ~/Desktop/mount_shared.sh < Shared folder Name> Please Note < Shared folder Name> is from the previous section. 22

23 Step 6 Reboot the VM when setup script is completed. 23

24 10. OSX Installation/Configuration To ensure program stability, Solar Panel Estimator will run under Virtual Machine on Mac OSX System. See Chapter 8.1 for System Requirement. Step 1 To setup SPE on OSX, first connect the SPE SSD on OSX machine. After all USB Bus drivers are installed, you will the icon shown below on desktop. Double click on it to show access the drive in Finder. Navigate to SolarPanelEstimator folder and copy the SolarPanelEstimator_v1.3 folder to the locate machine. Please remember the location, as we will need it later. Step 2 Next Navigate to OSX folder and double click on VirtualBox OSX.dmg Double Click on VirtualBox.pkg to start the installation process 24

25 Click Continue Follow the instruction from VirtualBox installer to complete installation. Keep all setting as is. 25

26 Step 3 When installation is completed, run VirtualBox and Import SPE Image by: File à Import Appliance from the top manual. Select the SolarPanelEstimator_v1.3 Image that you copied during the first step and click Next. 26

27 Step 4 After Importing the SolarPanelEstimator_v1.3, you should see the screen shown below. Before starting the Virtual Machine (VM), click on Settings à General à Advance, Change Drag n Drop to Bidirectional 27

28 Next, setup shared folder between Host and Guest by navigating to Settings à Shared Folders à Add new folder (folder with the + icon) Select the Folder you wish to share with the Guest (VM). This folder will use to store any output from Shared Folder name, Please remember this name, as we will use it in the next Section. Check Auto- mount Step 5 When all configurations are completed, you are ready to start the virtual Machine. After VM started up, open up Terminal by pressing Ctrl + Alt + T and execute the following command on Desktop. sh ~/Desktop/mount_shared.sh < Shared folder Name> Please Note < Shared folder Name> is from the previous section. 28

29 Step 6 Reboot the VM when setup script is completed. 29

30 11. Linux Installation/Configuration Supported Linux version: Ubuntu 12.04LTS Ubuntu 14.04LTS 11.1 Ubuntu SolarPanelEstimator is tested on Ubuntu 12.04LTS and Ubuntu 14.04LTS. Step 1 To install SolarPanelEstimator on Ubuntu, first connect the SPE SSD on Windows machine. After all USB Bus drivers are installed you will see the drive being mounted on the left manual bar. Navigate to SolarPanelEstimator folder and copy the SolarPanelEstimator_v1.3 folder to the locate machine. Please remember the location, as we will need it later. Step 2 Open up Terminal by pressing Ctrl + Alt + T and execute the following command. cd <path to SolarPanelEstimator_v1.3 >/./configure echo < Result folder Name> > OutDataPath.path For example, if you copied to Desktop, the path will be: ~/Desktop/SolarPanelEstimator_v1.3 Step 3 Reboot the system when setup is completed. 30

31 12. Execution When all setup is complete, SolarPanelEstimator should be running in the background. To verify, simply open up a web browser, and enter the following URL: You will see the following screen: Step 1 Enter the zip code you want to perform calculation on (by default is Davis, CA 95616) and click zoom: 31

32 Step 2 Select area selection mode from the drop down manual: Manually Draw Rectangle Pin Point Manually Draw Rectangle Click anywhere on the map and drag to select a bounding box of area you wish to perform calculation on. Next, click Perform Calculation to start the solar panel estimation. To clear any miss- drew Polygons on the map, click on Clear All Polygons. Note: Please wait until File Manager show the results folder. DO NOT click Perform Calculation more than once. Pin Point Click anywhere on the map to select a pinpoint location as upper left corner of the bounding box. By providing the rows and columns, program will automatically select corresponding bounding box to perform calculation. 32

33 Next, click Perform Calculation to start the solar panel estimation. To clear any miss- drew Polygons on the map, click on Clear All Polygons. Note: Please wait until File Manager show the results folder. DO NOT click Perform Calculation more than once. Step 3 When all calculation is complete, location of the result (see Chapter 7.2 for results details) will show in File Manager window. Note: File Manager will not popup until all calculation is completed. 33

34 13. Future Development Improvement to solar panel detection algorithm. Improvement to user interface. Better roof detection algorithm. Perform calculation by area within a zip code and city limit. Detect south facing roof. 34

35 14. Reference [1] Google Geocoding API, [2] OpenCV, [3] Python- Flask, 35

36 15. Contact Us Hiu Hong Yu Student Research Assistant Advanced Highway Maintenance & Construction Technology Research Center (AHMCT) 1308 Academic Surge One Shields Avenue University of California, Davis Davis, California Phone : (510) hiuyu@ucdavis.edu Responsibility: Server Back- End Terence Wu Software Developer Watershed Sciences One Shields Avenue University of California, Davis Davis, California Phone: (510) tewu@ucdavis.edu Responsibility: Server Front- End Wen Xi Zhang Student Research Assistant Watershed One Shields Avenue University of California, Davis Davis, California Phone: (510) wenzhang@ucdavis.edu Responsibility: Image Processing Raymond Christy Phone: (510) rchristy@ucdavis.edu Responsibility: Testing 36