• ⚡🏭 Power Industry Engineer/ Project Manager | 📊 Data Enthusiast | 🌎 New Jersey, USA

I'm Bill—a power industry professional with 20+ years of experience in power generation. My background as a mechanical engineer led to me a role as project manager where I led the development and execution of power generation projects. These days, I'm diving into data science, visualization, and machine learning with the intention of using it as a tool to uncover insights and improve decision making in power project development, design, procurement, construction and operations.

🛠️ My Current Skills:

• Project Management and Engineering:

  • Power Project Development
  • Management of Preliminary Engineering, Permitting and Interconnection Support
  • Management of Engineering & Detailed Design
  • Heat Balance Development using GT-Pro and Steam-Pro
  • Life Cycle Cost Analysis to guide Engineering Alternative Decision Making
  • Emerging Technology Due Diligence

• Data Analysis

  • SQL
  • Python Data Analysis:
    • Data Manipulation, Cleanup with Pandas, Numpy
    • Data Visualization and Exploratory Data Analysis with scipy.stats, Matplotlib, Seaborn and Plotly
    • Machine Learning Algorithms using python such as Linear Regression, Decision Trees, Random Forest, XGBoost, for Classification and Regression
    • Time Series Analysis using Exponential Smoothing, ARIMA, SARIMA
    • Building basic dashboards with Plotly Dash
  • POWER BI

🎯 My Current Focus Areas for New Skills:

• Learning additional Time Series Analysis methods including Facebook Prophet and Neural Networks
• Using Data to predict power plant performance and detect problems.

Outside of work, I enjoy the outdoors; hiking with my dog, bike riding, skiing, taking in the beach/ocean and cheering on Rutgers football/basketball. Throw in travel with any of those, and it only gets better! 🌍🏔️🚴‍♂️

• 🏅 Professional Engineer: State of New York
• 🎓 IBM Data Science Specialization Certificate
• 🤖 Generative AI for Data Scientists

📧 Email: wlouer@gmail.com
💼 LinkedIn: Bill Louer

A small percentage of power projects that are developed are actually permitted, and built. Development is a risky business. So any efforts to use data analysis, visualization or machine learning to apply resources to the right projects, over the wrong projects, or to identify and target improvement of high risk areas on individual projects in development could yield a significant payback.