Short-term forecasting of residential electricity demand is an important task for utilities. Yet, many small and medium-sized utilities still use simple forecasting approaches such as Synthesized Load Profiles, which treat residential households similarly and neither account for renewable energy installations nor novel large consumers (e.g., heat pumps, electric vehicles). The effectiveness of such "one-fits-all" approaches in future grid states--where decentral generation and sector coupling increases--are questionable. Our study challenges these forecasting practices and investigates whether Machine Learning (ML) approaches are suited to predict electricity demand in today's and in future grid states. We use real smart meter data from 3,511 households in Germany over 34 months. We extrapolate this data with future grid states (i.e., increased decentral generation and storage) based on a digital twin of a local energy system. Our results show that Long Short-Term Memory (LSTM) approaches outperform SLPs as well as simple benchmark estimators with up to 68.5% lower Root Mean Squared Error for a day-ahead forecast, especially in future grid states. Nevertheless, all prediction approaches perform worse in future grid states. Our findings therefore reinforce the need (a) for utilities and grid operators to employ ML approaches instead of traditional demand prediction methods in future grid states and (b) to prepare current ML methods for future grid states.

Around the world, and particularly in Australia, energy traders are trying to get their minds, and their algorithms, around the complexities of trading in variable wind and solar projects and super-fast battery storage installations. Maybe they should give up now, and hand it over to artificial intelligence. US-based software-as-a-service platform provider AMS says automated trading systems for batteries and renewable energy projects using deep learning and artificial intelligence can out-compete the best human traders, by around a factor of five. With the deployment of large-scale energy storage systems occurring at an ever-increasing rate, this is critical – not just for the ability to make money out of the markets, but also for the ongoing operation of the National Electricity Market itself. Traditional generators only need to maximise their generation during periods of sufficiently high energy prices.