Energy Forecasting
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.
Operational LLM AI-Agent for Energy Community BESS Management
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.
Fundamental of Electricity Markets (Part 1)
Introduction to electricity market design, demand/supply curves, marginal utility, equilibrium pricing, and social surplus.
Self-Directed Learning: Renewables in Electricity Markets
Study notes on market structures, pricing mechanisms, day-ahead clearing, intraday trading, balancing markets, and offering strategies.
Smart Heat Pump Control: Reinforcement Learning and Digital Twins
Building a custom Gym environment for smart heat pump control with thermal storage using Stockholm temperature data and ENTSO-E day-ahead electricity prices.
Heat Pump Digital Twins: Modeling and Hybrid State Estimation
An introduction to heat pump technologies, thermodynamic cycles, coefficient of performance (COP), and building physical-informed digital twins.
Ocean Wave Energy Flux Forecasting: Probabilistic Forecasting (Part 3)
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.
Ocean Wave Energy Flux Forecasting: A Stacking Ensemble (Part 2)
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.
Ocean Wave Energy Flux Forecasting using LightGBM (Part 1)
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.
Introduction to Wave Energy
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.
Stochastic Optimisation for Energy Storage (Part 2)
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.
Stochastic Optimisation for Energy Storage (Part 1)
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.
Probabilistic Electricity Price Forecasting (Part 3)
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.
Probabilistic Electricity Price Forecasting (Part 2)
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.
Probabilistic Electricity Price Forecasting (Part 1)
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.