MixForecast: Mixer-Enhanced Foundation Model for Load Forecasting

Short-term Load Forecasting (STLF) for buildings is essential for optimizing energy management and supporting renewable energy integration, but traditional models often struggle with generalization across diverse building profiles. While recent Time Series Foundation Models (TSFMs) show promise, they remain underexplored for STLF. In this paper, we introduce MixForecast, a novel TSFM for universal energy forecasting. The MixForecast architecture is based on the TSMixer block ensembling technique to provide accurate and robust load forecasting for smart meter time series. In particular, MixForecast is designed with fewer parameters approx 0.19M than existing TSFMs, making it efficient and suitable for deployment in individual buildings. We trained MixForecast using hourly energy data from 63K buildings and evaluated its performance on a test set of 1,000 commercial and residential buildings around the world. We compared MixForecast against pre-trained TSFMs such as Tiny Time Mixers, Lag-Llama, Moirai, and Chronos, in both zero-shot and fine-tuned settings, as well as against traditional models. The model demonstrates superior accuracy and adaptability, excelling in various building profiles. Its lightweight design, combined with strong forecasting performance, establishes MixForecast as a versatile and efficient model for STLF, advancing energy management while promoting sustainable energy practices.