This is like a very smart thermostat for the power grid: it looks at past electricity usage patterns (hour by hour, day by day) and learns to predict how much energy people will use in the near future using two types of math "brains" (XGBoost and LSTM).
Utility companies and grid operators need accurate short‑ and medium‑term forecasts of energy consumption to plan generation, purchases, storage, and grid operations. Current methods can be inaccurate or slow to adapt to changing patterns (weather, behavior, EVs, solar), leading to over‑ or under‑supply, higher costs, and reliability risks. This work evaluates advanced ML models (XGBoost and LSTM) to improve forecast accuracy for energy consumption time series.
The defensibility comes mainly from high‑quality, granular historical load and weather data, plus domain‑specific feature engineering and model tuning for a given grid/region. The underlying algorithms (XGBoost, LSTM) are broadly available, so sustained advantage depends on proprietary data, integrated forecasting workflows, and how tightly this is coupled with grid operations and trading systems.
Classical-ML (Scikit/XGBoost)
Time-Series DB
Medium (Integration logic)
Training and inference latency for large multivariate time series (many meters, long history), and the need to frequently retrain or update models as demand patterns shift; data quality and real‑time ingestion from metering/SCADA systems can also constrain performance.
Early Majority
Compared with traditional statistical methods (e.g., ARIMA) commonly used in utilities, this approach leverages modern ML algorithms—gradient boosting (XGBoost) and deep learning (LSTM)—to capture nonlinear relationships and complex temporal dependencies in energy consumption. The combination and comparative evaluation of both methods on energy load forecasting positions it as a more advanced, data‑driven forecasting framework that can outperform legacy models when sufficient data and computation are available.