A leading company in the energy sector needs to optimise the forecasting of natural gas and electricity consumption for different market segments.
The goal is to improve supply planning ensure effective grid balancing, and minimise imbalance charges. In this context, there is a need for a tool capable of accurately estimating daily consumption, integrating various real-time variables.
At Moxoff we have developed a demand forecasting system for gas and electricity consumption, based on Machine Learning technologies such as neural networks and model ensembles to improve predictive accuracy.
The model dynamically adapts to different market segments (residential and industrial), integrating historical data and contextual variables such as weather conditions and economic events. This approach enables the generation of granular forecasts, differentiated by geographic area and type of consumption. The system is designed to evolve over time, maintaining high accuracy even in the face of regulatory or market changes, and to support more efficient, precise and localised management of energy consumption.
