Machine Learning
Stream-Based Electricity Load Forecast
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Proceedings of the 5th ACM international conference on Distributed event-based system
ANNSTLF-a neural-network-based electric load forecasting system
IEEE Transactions on Neural Networks
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The increasing use of renewable energy is leading to a paradigm shift in operating electrical grids. Production is moving away from centralized power plants to decentralized sources like solar panels and windmills. One consequence of this development is the need for managing supply and demand in local distribution grids in a "smart" way, which also implies the capability to forecast the demand for electric power closer to the end consumer and on shorter time scales than today. In this paper, we describe a system prototype for electricity demand forecasting based on highly disaggregated data from sensors deployed in homes and evaluate its performance both with respect to forecasting accuracy and ICT resource requirements. The data we use for our evaluation was collected in a pilot trial. Our system prototype combines complex event processing with state-of-the-art forecasting capabilities. For short-term forecasts, we observed average error reductions of up to 98 percentage points compared to average demand profiles. Our experiments also show the applicability of our approach at large scale. We were able to run the forecasting service for 1,000 households in parallel on one off-the-shelf server.