Analisi dati da multisensore ambientale Abstract “At the Energy Center plant in Turin, measuring, understanding, and predicting thermal energy consumption plays a crucial role in improving operational efficiency and supporting sustainable energy use. This thesis investigates data-driven methods for forecasting thermal energy demand, using real sensor data collected from the plant over a five-month period between October 2024 and March 2025. The dataset includes high-resolution temperature readings from multiple points in the district heating network, along with cumulative energy consumption measurements. To make the data suitable for predictive modeling, several preprocessing steps were applied: aligning time series with different sampling rates, interpolating missing values, filtering out anomalies, and performing feature engineering.