Rapid decarbonisation and the proliferation of wind and solar generation are transforming power systems. These changes create volatility that must be managed through flexible demand and responsive reserves. This thesis presents a comprehensive investigation into how machine‑learning models—particularly Random Forests—can support demand response by predicting system regulation states and translating those predictions into actionable control logic for an industrial load optimization. After reviewing more than adozen peer‑reviewed studies on energy forecasting, demand response and market design, the work constructs a rich dataset of meteorological and market variables aligned at hourly resolution. Cyclical encoding and interaction features capture the inherent periodicity of time and nonlinear relationships between weather and market signals. A Random Forest classifierand a regression model are trained on manual frequency restoration reserve(mFRR) activation data for 2023–2025. The classifier achieves an overall accuracy of 79 %, while the regression model (dedicated for the hourly loadprediction) explains 77 % of the variance in factory load. The predictions are then integrated into a scheduling algorithm that adjusts the load of a 30 MW industrial plant within the Swedish bidding zone SE1, respecting internal stock constraints for each process area. Compared with a baseline without cyclical and interaction features, the enhanced model improves the up‑regulation F1‑score from 0.65 to 0.74. The thesis concludes that interpretable machine‑learning, augmented by domain‑specific feature engineering,provides a robust and operationally viable foundation to maximize the bidingstrategy on mFRR market by integrating industrial consumers into balancing markets and facilitating the energy transition. We also quantify cross –modelalignment: using the model in August 2025, the schedule reduced load in~80% of hours classified as UP, stayed near-neutral on NONE hours, and achieved overall alignment of ~31% under a ±0.1 MW neutrality band(≈50% under ±0.5 MW).