I’m passionate about applying computational methods, machine learning, and large-scale optimization to solve real-world energy system challenges.

With over two years of experience in the oil and gas industry and a First-Class Degree in Civil Engineering, I am now actively transitioning into computational energy systems research.
My work focuses on building machine learning models, automating processes, and using data to improve energy decision-making.

• Time-series forecasting using ARIMA, SARIMA, and LSTM models
• Process automation and data visualization for energy systems
• Large-scale stochastic optimization and smart energy systems
• Preparing for PhD applications in computational energy systems

• 🔹 Energy Consumption Forecasting using LSTM
  Developed a TensorFlow LSTM model for hourly energy forecasting that achieved over 95% improvement in prediction accuracy compared to traditional models.

• 🔹 Energy Forecasting using ARIMA and SARIMA
  Built statistical time-series models to forecast hourly energy consumption and benchmarked them against advanced deep learning models.

• 🔹 Process Automation in Field Reporting using VBA
  Automated complex field reporting workflows, reducing reporting time by over 90%.

• 🔹 Energy Systems Visualization Dashboard Building an interactive dashboard to visualize energy trends and forecast outputs.

• Python (Pandas, NumPy, TensorFlow, Scikit-learn, Matplotlib)
• VBA for process automation
• Time-Series Forecasting: ARIMA, SARIMA, LSTM
• Dashboard Design: Matplotlib, Plotly

To contribute to interdisciplinary research that improves the resilience and sustainability of global energy systems using machine learning, computational optimization, and data-driven tools.

• LinkedIn Profile
• GitHub: jameslucasetot256