Transfer Learning in Transformer-Based Demand Forecasting For Home
  Energy Management System
Gargya Gokhale Jonas Van Gompel Bert Claessens Chris Develder
Increasingly, homeowners opt for photovoltaic (PV) systems and/or battery
storage to minimize their energy bills and maximize renewable energy usage.
This has spurred the development of advanced control algorithms that maximally
achieve those goals. However, a common challenge faced while developing such
controllers is the unavailability of accurate forecasts of household power
consumption, especially for shorter time resolutions (15 minutes) and in a
data-efficient manner. In this paper, we analyze how transfer learning can help
by exploiting data from multiple households to improve a single house's load
forecasting. Specifically, we train an advanced forecasting model (a temporal
fusion transformer) using data from multiple different households, and then
finetune this global model on a new household with limited data (i.e. only a
few days). The obtained models are used for forecasting power consumption of
the household for the next 24 hours~(day-ahead) at a time resolution of 15
minutes, with the intention of using these forecasts in advanced controllers
such as Model Predictive Control. We show the benefit of this transfer learning
setup versus solely using the individual new household's data, both in terms of
(i) forecasting accuracy ($\sim$15\% MAE reduction) and (ii) control
performance ($\sim$2\% energy cost reduction), using real-world household data.