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Abstract
Following the ongoing transformation of the European power system, in the future, it will be necessary to locally balance the increasing share of decentralised renewable energy supply. Therefore, a reliable short-term load forecast at the level of single buildings is required. In this study, we use a forecaster, which is based on K-nearest neighbours approach and was introduced in an earlier publication, on three buildings of Smart City Demo Aspern project. The authors demonstrate how this forecaster can be applied on different buildings without any manual setup or parametrisation, showing that it is viable to replace load-profiling solutions for predicting electricity consumption at the level of single buildings.
Highlights
The increasing share of renewable energy sources within electricity generation leads to new challenges for the power system infrastructure
In a recent publication [9], we have proposed an initial approach for such forecaster based on the K-nearest neighbours (KNN) method
We have applied a forecaster based on the KNN technique to predict daily load curves of three different smart buildings participating in Smart City Demo Aspern (SCDA) project, achieving convincing forecast accuracy
Summary
The increasing share of renewable energy sources within electricity generation leads to new challenges for the power system infrastructure. With the increasing share of decentralised supply connected to the distribution grid, balancing of power generation and consumption will have to be done locally, at the low-voltage level For this purpose, SLPs are inappropriate as they only rudimentarily reflect the diversity and highly stochastic nature of the building electricity demand. It is easier to forecast large aggregated loads present in higher domains [5] and, until now, there have been only few attempts [6,7,8] to adopt those approaches at the building level, where the load aggregation is much smaller Such propositions are, mostly, done for a specific building and it is still an ongoing challenge to develop a universally applicable forecaster that can be applied on various different buildings disregarding their size or purpose, delivering reliable accuracy. Afterwards, we explain the forecasting approach of our KNN forecaster and present the results concluding this study
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