21/08/2023
Developed five key accuracy metrics to evaluate the performance of 20 mainstream oil and gas price forecasts and forward curves, providing critical insights for 23 proprietary hedge funds. Created an interactive Tableau dashboard to visualize price forecasts and accuracy metrics for these datasets, streamlining manual reporting and saving around 5 hours each week.
Built a powerful string matching engine to deduplicate over 40,000 company names, accounting for variations in legal structures, abbreviations, omissions, and typographical errors. Leveraged Natural Language Processing (NLP) with a TF-IDF vectorizer to ensure efficient and accurate company name standardization.
Developed an interactive web interface using Python Dash and Plotly to provide insights into global energy trends and predict fossil fuel prices. Applied advanced time series models, including Seasonal ARIMA, STL Decomposition, Exponential Smoothing, and Prophet, to forecast energy prices and present valuable data-driven insights.
Used Long Short-Term Memory (LSTM) neural networks combined with NLP techniques to analyze the sentiments behind energy-related news headlines and predict how they affect the index value in the oil and gas industry. This approach provided a deeper understanding of market dynamics driven by sentiment, offering critical insights into market behavior.
Applied multivariate regression to predict future capital expenditures and operating costs of U.S. power plants, accounting for various fuel and technology types. This analysis enabled more accurate financial forecasting, supporting decision-making across different plant configurations and cost structures.
Built a Python application to map U.S. gas power plants to their nearest gas hub district using the K-Means clustering algorithm and Vincenty distance function. This mapping ensured that each power plant was within a 12-mile radius of every other plant in the hub, helping optimize distribution networks and improve logistical efficiency.