Highly accurate power demand forecasting represents one of key challenges of Smart Grid applications. In this setting, a large number of Smart Meters produces huge amounts of data that need to be processed to predict the load requested by the grid. Due to the high dimensionality of the problem, this often results in the adoption of simple aggregation strategies for the power that fail in capturing the relational information existing among the different types of user. A possible alternative, known as Cluster-based Aggregate Forecasting, consists in clustering the load profiles and, on top of that, building predictors of the aggregate at the cluster-level. In this work we explore the technique in the context of predictors based on deep recurrent neural networks and address the scalability issues presenting neural architectures adequate to process cluster-level aggregates. The proposed methods are finally evaluated both on a publicly available benchmark and a heterogenous dataset of Smart Meter data from an entire, medium-sized, Swiss town.

Cluster-based Aggregate Load Forecasting with Deep Neural Networks

Alippi C.
2020-01-01

Abstract

Highly accurate power demand forecasting represents one of key challenges of Smart Grid applications. In this setting, a large number of Smart Meters produces huge amounts of data that need to be processed to predict the load requested by the grid. Due to the high dimensionality of the problem, this often results in the adoption of simple aggregation strategies for the power that fail in capturing the relational information existing among the different types of user. A possible alternative, known as Cluster-based Aggregate Forecasting, consists in clustering the load profiles and, on top of that, building predictors of the aggregate at the cluster-level. In this work we explore the technique in the context of predictors based on deep recurrent neural networks and address the scalability issues presenting neural architectures adequate to process cluster-level aggregates. The proposed methods are finally evaluated both on a publicly available benchmark and a heterogenous dataset of Smart Meter data from an entire, medium-sized, Swiss town.
2020
Proceedings of the International Joint Conference on Neural Networks
978-1-7281-6926-2
deep learning
short-term load forecasting
smart grid
time-series clustering
time-series forecasting
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1167399
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