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Implementing Quantile Regression and Quantile Regression Forest for DK1 day-ahead prices, evaluated with walk-forward cross-validation: real code, real results, and what changes once a single train/test split becomes four folds spread across different seasons.
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Bootstrapped residuals for DK1 day-ahead prices: in-sample vs out-of-sample residuals, binning by predicted value, and multiple interval levels, adapted from skforecast’s bootstrapped-residuals guide and evaluated on real DK1 data.
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From deterministic to stochastic decision-making: two-stage and multi-stage stochastic programs, common solution approaches, and why battery storage scheduling needs to account for uncertain prices, demand, and generation.
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A working two-stage stochastic battery dispatch model, solved in Pyomo, with scenarios built from real quantile forecasts instead of hand-picked historical days. Compared against a naive single-forecast schedule and a perfect-foresight upper bound on a real DK1 test day.
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Why ocean waves are a large, steady, low-emission energy resource, the physics that governs them, the eight main types of wave energy converter, and how wave conditions are actually measured.
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Forecasting significant wave height and mean wave period 1 to 48 hours ahead with LightGBM on ERA5 ocean reanalysis data off northern Portugal, then combining both into a wave power flux estimate.
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Stacking Ridge Regression, Random Forest, and LightGBM with a ridge meta-model to forecast significant wave height and mean wave period, and checking whether it recovers the long-lead skill that pure LightGBM lost in Part 1.
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Transitioning from point forecasts to probabilistic predictions. Comparing Quantile Regression (QR), Quantile Regression Forest (QRF), and Bootstrapped Residuals to estimate uncertainty in wave height and mean wave period.
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An introduction to heat pump technologies, thermodynamic cycles, coefficient of performance (COP), and building physical-informed digital twins.
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Building a custom Gym environment for smart heat pump control with thermal storage using Stockholm temperature data and ENTSO-E day-ahead electricity prices.
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Study notes on market structures, pricing mechanisms, day-ahead clearing, intraday trading, balancing markets, and offering strategies.
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Introduction to electricity market design, demand/supply curves, marginal utility, equilibrium pricing, and social surplus.
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A conceptual architecture and a working prototype for an LLM-based agent that advises on battery dispatch: day-ahead price forecasting with LightGBM, BESS sizing and scheduling in Pyomo, and a provider-agnostic ReAct tool-use agent on top.
This page gathers projects and articles focusing on mathematical modeling and machine learning applications for forecasting energy systems, including probabilistic electricity price forecasting, load forecasting, and renewable energy generation.
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An introduction to probabilistic electricity price forecasting: the European and Danish power markets, quantiles and prediction intervals, Quantile Regression and Quantile Regression Forest, and evaluation metrics such as Pinball Loss, CRPS, and the PIT histogram.
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This is my 1st post about Machine Learning (ML). This is a topic I learnt from the book “An Introduction to Statistical Learning”.
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K‑Nearest Neighbors (KNN) is a simple, non‑parametric machine learning algorithm that makes predictions based on the labels of the closest data points in the training set. It is supervised (it needs labeled examples) and can be used for both classification and regression tasks.”
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This post demonstrates how to model wind turbine power using KNN regression with simulated wind-farm data.
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This post explains how Random Forest works, step by step. It covers bootstrap sampling, random feature selection, decision tree growth, and aggregation.
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This post explains how Support Vector Machines (SVM and SVR) work, from the hyperplane concept and the kernel trick to the epsilon-insensitive tube used in regression.
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In this post, I will guide you through creating a Flappy Bird game, originally developed by Vietnamese creator Dong Ha NGUYEN. In part 2, I will explain a simple Aritifical Neural Network (ANN) model to make Flappy Bird A.I.
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In this part 2, I will explain a simple Aritifical Neural Network (ANN) model to make Flappy Bird A.I.
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An intuitive introduction to Reinforcement Learning. Learn the core concepts, Markov Decision Processes, key training trade-offs, and algorithm taxonomies.
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Understand the theory, constraint geometry, and step-by-step numerical calculation of Elastic Net regression.
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Learn how to solve a Rubik’s Cube with interactive 3D visualizations, custom algorithms walkthrough, and Python Reinforcement Learning models.
This page collects all my posts on topics I find interesting about how to learn Machine Learning 😁💻.
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Case-Based Module: Evaluating algae-derived Sustainable Aviation Fuel (SAF) pathways; comprehensive study for aviation decarbonization.
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Operational planning, sizing, and optimization assignment for Université de Liège; my favorite DENSYS course that sparked my love for programming!
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Multi-criteria evaluation and future deployment scenario modeling of biomass-to-liquid (BtL) conversion technologies.
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Feasibility study of 120 kW SOFC system achieving 116.88 kW net output with 16.33-year payback for Novara WWTP biogas valorization.
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Repowering feasibility study replacing three aging Vestas V44-600 turbines with single modern V82-1.65 MW turbine, achieving 33% energy increase and 38% LCOE reduction.
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MSc thesis at rebase.energy (Stockholm): forecasting wind turbine icing power losses at 1-36 hour lead times using SCADA and ERA5-Land data, an extended IEA Wind Task 19 framework, and a two-stage LightGBM classifier-regressor.
This page gathers projects and reports completed during my Master's program studies.
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Integrated LCA-ML framework achieving 12,600× computational acceleration for rapid environmental optimization of North Sea offshore wind turbines.
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In this first post of wind energy, I will walk you through the topic icing formation. In the cold climate regions, icing on wind turbines is significant problem, impacting both performance and physical reliability…
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In this first post of wind energy, I will walk you through the IEA Task 19 - Icing events classifcation and and power loss calculation…
This page gathers all my posts related to wind energy, covering topics I find particularly interesting—ranging from atmospheric physics and forecasting to power systems and cold-climate effects ✇🌫⚡❄️.
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Guideline for studying exam
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Offshore wind farms become larger and wind turbines become higher and higher every year. And they stir up the air and change how low clounds form over the ocean.
Published in International Journal of Hydrogen Energy, 2024
Techno-economic comparison of export pathways and energy carriers for large-scale hydrogen export from Vietnam to Asia.
Recommended citation: Dang-Chuong Ta; Thanh-Hoang Le; Hoang-Luong Pham. (2024). "An Assessment of the Potential for Large-Scale Hydrogen Export from Vietnam to Asian Countries: Techno-Economic Analysis, Transport Options, and Energy Carrier Comparison." International Journal of Hydrogen Energy, Impact Factor: 9.2. [10.1016/j.ijhydene.2024.04.033](https://doi.org/10.1016/j.ijhydene.2024.04.033)
Published in Energy Conversion and Management, 2025
Feasibility study of hydrogen co-firing in Vietnam’s gas power plants for the period 2035–2050, analyzing technical and economic impacts.
Recommended citation: Dang-Chuong Ta; Thanh-Hoang Le; Long Van Phan; Hoang-Luong Pham. (2025). "Feasibility Analysis of Hydrogen Co-Firing in Vietnam’s Gas Power Plants for the Period 2035–2050." Energy Conversion and Management, Impact Factor: 11.8. [10.1016/j.enconman.2025.120192](https://doi.org/10.1016/j.enconman.2025.120192)
Published in , 1900
All of the journal papers which I have contributed to are freely available on this website. If you have trouble accessing any papers related to my work or that of my lab members please email me directly.