Github Repo: https://github.com/codeamt/WindFarmSpotter

Github回購： https : //github.com/codeamt/WindFarmSpotter

This is a series:

這是一個系列：

• Part 1: A Brief Introduction on Leveraging Edge Devices and Embedded AI to Track the U.S.Wind Energy Footprint (You are Here)

  第1部分：有關利用邊緣設備和嵌入式AI跟蹤USWind能源足跡的簡要介紹(您在這里)

• Part 2: An Approach to Satelite Arial Image Data Generation and Automation with Google Earth Engine, Basemap, and Colab

  第2部分： 使用Google Earth Engine，底圖和Colab進行衛星Arial圖像數據生成和自動化的方法

• Part 3: Experimenting with Memory, Efficiency, and Scaling Input Resolution using a Fast.ai v3 Training Pipeline

  第3部分： 使用Fast.ai v3培訓管道試驗內存，效率和擴展輸入分辨率

• Part 4: Running Inference Tests: Swift-Python Interoperability, and Hardware Acceleration

  第4部分：運行推理測試：Swift-Python互操作性和硬件加速

• Part 5: Spinning Up Inference APIs — Flask (Just Python) v. Kitura (Python & Swift)

  第5部分 ：旋轉推理API — Flask(僅Python)訴Kitura(Python和Swift)

• Part 6: Containerizing Deployments for Web, ARMv8/Jetson NVIDIA Series, and SWAP Hardware Platforms

  第6部分： Web，ARMv8 / Jetson NVIDIA系列和SWAP硬件平臺的容器化部署

Recently, I completed a data science and software engineering project as part of a hiring pipeline.

最近，我在招聘流程中完成了一個數據科學和軟件工程項目。

The company (and I’ll keep the entity anonymous for now) takes a novel approach to the technical interview — lending applicants an NVIDIA Jetson TX2 GPU with free range to execute on a deep learning area of interest.

該公司(我現在將實體保持匿名)將采用一種新穎的方式進行技術面試-向申請人提供具有自由范圍的NVIDIA Jetson TX2 GPU，以便在感興趣的深度學習領域內執行。

關注的領域：風電場—確定潛在的擴展區域，這意味著通過公噸減少碳排放(CO2) (Area of Interest: Wind Farms — Identifying Potential Areas of Expansion Means Reducing Carbon Emission (CO2) by the Metric Ton)

Given the election season and lots of mention of shifting to renewable energy sources being key to lowering our Carbon Footprint, I took this opportunity to learn more about various forms of energy and realized Wind Energy has lots to offer!

鑒于選舉季節和降低可再生能源足跡的關鍵，很多人都提到轉向可再生能源，因此我借此機會了解了更多有關各種形式能源的信息，并意識到風能提供了很多！

During my research, I found this fact sheet published by the University of Michigan that laid out the value propositions of Wind Energy. The publication highlighted that:

在研究過程中，我發現了密歇根大學發布的這份情況說明書 ，列出了風能的價值主張。 該出版物強調：

• Increasing Wind Capacity by 1 GigaWatt (GW) avoids the need for Carbon (CO2) Emission by a couple of million metric tons and reduces the need for Water (for Power plants) by roughly a million gallons.
  將風力發電能力提高1吉瓦(GW)，可避免將碳(CO2)排放減少幾百萬公噸，并減少大約一百萬加侖的水(用于發電廠)。

• Previous research from 2015 found that if Wind Turbines — the central technology of Wind Farms — generated 35% of our electricity, this would eliminate 510 billion kg of CO2 emissions annually.

  2015年的先前研究發現，如果風力渦輪機 ( 風力發電場的核心技術)產生了我們35％的電力，那么每年將減少5100億公斤的二氧化碳排放。

• Wind Farms do not disturb the peace. Given a 350meter radius, Wind Farms emit roughly the same amount of noise (35–45 decibels) as a quiet bedroom (35 decibels) and less noise than a car driving 40mph (55 decibels).

  風電場不會干擾和平。 在半徑為350米的情況下，風電場發出的噪音與安靜的臥室(35分貝)大致相同(35-45分貝)，并且比以40英里/小時的速度行駛(55分貝)的汽車要少 。

• Wind Energy is very cost-effective. In terms of residential energy prices, in 2016, typical energy quotes were based on the rate of 12.9￠/kWh, where wind energy would only be 2￠/kWh. (That’s right, wind energy would make your electricity bill 6x cheaper!)
  風能非常劃算。 在居民能源價格方面，2016年，典型能源報價基于12.9美分/千瓦時的價格，而風能僅為2美分/千瓦時。 (是的，風能會使您的電費便宜6倍！)
• For Wind Farmers, working on large capacity projects (defined in the fact sheet as >= 83 acres), the ROI ratio is $4 to $1.
  對于從事大型項目(在情況說明書中定義為> = 83英畝)的風力發電場，ROI比率為4：1。

Learning about this market has been a whirlwind, to say the least.

至少可以說，了解這個市場是一個旋風。

All this new knowledge made me wonder if data science/deep learning and specifically, computer vision, could help in “spotting potential” regions of interest for new Wind Farm projects and this initial inquiry led to the core idea of my project Wind Farm Spotter: an inference engine for classifying the capacity of existing land-based Wind Farms and potential capacity of unoccupied locations from satellite images.

所有這些新知識使我想知道，數據科學/深度學習，特別是計算機視覺是否可以幫助“發現”新風電場項目的潛在感興趣區域，而最初的詢問導致了我的項目“風電場觀測者”的核心思想：推理引擎，用于根據衛星圖像對現有陸上風電場的容量和未占用位置的潛在容量進行分類。

項目范圍：開發用于風電場觀測器的機器學習管道的端到端演練 (Project Scope: An End-to-End Walkthrough of Developing a Machine Learning Pipeline for Wind Farm Spotter)

In subsequent posts, I’ll share my thoughts and findings on developing an end-to-end Machine Learning Pipeline and creating inference engine deployments for web and fog/edge SWAP Hardware Architecture.

在隨后的文章中，我將分享我對開發端到端機器學習管道以及為Web和fog / edge SWAP硬件架構創建推理引擎部署的想法和發現。

Tools and Environment:

工具和環境：

Software used to develop this project include:

用于開發此項目的軟件包括：

• Google Earth Engine
  Google Earth Engine
• Basemap
  底圖
• ArcGIS API Service
  ArcGIS API服務
• PyTorch 1.1 / Torchvision
  PyTorch 1.1 / Torchvision
• pytorchcv
  pytorchcv
• Fast.ai v3
  Fast.ai v3
• Python 3.6, Flask
  Python 3.6，燒瓶
• Swift 5.0.1, Kitura
  雨燕5.0.1，基圖拉
• Jetpack 4.3
  噴氣背包4.3
• XQuartz (X11)
  XQuartz(X11)
• Virtualenv
  虛擬環境
• Docker Community Edition, Edge
  Docker社區版，Edge

Environment:

環境：

• Google Drive
  Google云端硬碟
• Google Colab
  Google Colab
• MacBook Pro
  MacBook Pro
• Jetson TX2
  杰特遜TX2
• Ubuntu 18.04.3
  Ubuntu 18.04.3

Stay tuned for future posts! The code repository for this series can be found here.

請繼續關注以后的帖子！ 該系列的代碼存儲庫可以在這里找到。

Keep Reading:

繼續閱讀：

Next Post: Part 2: An Approach to Satelite Arial Image Data Generation and Automation with Google Earth Engine, Basemap, and Colab

下一篇文章：第2部分： 使用Google Earth Engine，底圖和Colab進行衛星Arial圖像數據生成和自動化的方法