Time Series Representation Learning Applications for Power Analytics

Abstract

The uptake of solar power generation is on the rise. This necessitates more research into developing data-driven intelligent methods that can perform effective analytics over power generation data to inform strategies to improve solar power generation systems. In this paper, we consider the utility of time series representation learning for analytics over power generation data. WaRTEm, a representation learning method that focuses on learning time series representations that are invariant to local phase shifts, is the focus of our investigations in this paper. We identify two metadata attributes for power generation sequences, month and CellID, as attributes that embed useful notions of semantic similarity between time series sequences. We evaluate the effectiveness of WaRTEm representations, as against using the raw time series sequences, in alignment to the month and CellID labellings, using accuracy over 1NN retrieval as an evaluation framework. Through empirical evaluations, we identify that WaRTEm embeddings are consistently able to achieve better representations when evaluated on 1NN accuracy. We also identify some features of WaRTEm that are more suited for time series representation learning, which provides promising directions for future work.