- ⚡🏭 Power Industry Project Manager/Engineer | 📊 Data Enthusiast | 🌎 New Jersey, USA
I'm Bill—a power industry professional with 25+ years of experience in power generation. My background as a mechanical engineer led me to 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 and webapps with Flask and Plotly Dash
- POWER BI
🎯 My Current Focus Areas for New Skills:
- Learning additional Time Series Analysis methods including Facebook Prophet and Neural Networks
- Finding applications for machine learning and AI in power project development.
- 🏅 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.