Causal-fuzzy neural operators for smart energy forecasting: A multi context learning framework for grid aware load prediction

The increasing deployment of smart meters across residential zones has generated an abundance of granular energy consumption data. However, accurately forecasting such data remains challenging due to its inherent heterogeneity, context-dependence, and nonlinearity. Traditional machine learning models often fall short in capturing the underlying causal dynamics and fuzzy uncertainties present in household-level energy use. To overcome this, we present a hybrid forecasting framework, which combines neural operator architecture, fuzzy logic-based interpretability, and causal discovery. This helps the framework learn structured representations from high-dimensional, multi-contextual data. We tested our framework on a real-world dataset of 5567 households’ smart meters forecasting data, in the city of London. Our model performs better than other traditional regressors with a test R2 score of 0.8788, which is about 30 % better than other baseline techniques evaluated. The model shows how our design helps people understand the results better. It identifies important factors that affect energy use, such as humidity, maximum temperature, and household energy. This work makes energy demand forecasts more accurate and easier to explain. It also provides a foundation for using similar models in areas like smart grids, sustainable cities, and renewable energy systems, where cause and uncertainty exist together.