Combining embeddings and fuzzy time series for high-dimensional time series forecasting in internet of energy applications?

被引:9
作者
Bitencourt, Hugo Vinicius [1 ,2 ]
de Souza, Luiz Augusto Facury [2 ]
dos Santos, Matheus Cascalho [2 ]
Silva, Rodrigo [3 ]
de Lima e Silva, Petronio Candido [4 ]
Guimaraes, Frederico Gadelha [2 ]
机构
[1] Univ Fed Minas Gerais, Grad Program Elect Engn, Belo Horizonte, Brazil
[2] Univ Fed Minas Gerais, Machine Intelligence & Data Sci MINDS Lab, Belo Horizonte, Brazil
[3] Fed Univ Ouro Preto UFOP, Dept Comp Sci, Ouro Preto, Brazil
[4] Fed Inst Educ Sci & Technol Northern Minas Gerais, Januaria Campus, Quintino Bocaiuva, Brazil
关键词
Multivariate time series; Fuzzy time series; Embedding transformation; Time series forecasting; Smart buildings; Internet of energy; BIG DATA; MODELS; THINGS; IOT;
D O I
10.1016/j.energy.2023.127072
中图分类号
O414.1 [热力学];
学科分类号
摘要
High-dimensional time series increasingly arise in the Internet of Energy (IoE), given the use of multi-sensor environments and the two way communication between energy consumers and the smart grid. Therefore, methods that are capable of computing high-dimensional time series are of great value in smart building and IoE applications. Fuzzy Time Series (FTS) models stand out as data-driven non-parametric models of easy implementation and high accuracy. Unfortunately, the existing FTS models can be unfeasible if all variables were used to train the model. We present a new methodology named Embedding Fuzzy Time Series (EFTS), by applying a combination of data embedding transformation and FTS methods. The EFTS is an explainable and data-driven approach, which is flexible and adaptable for many smart building and IoE applications. The experimental results with three public datasets show that our methodology outperforms several machine learning based forecasting methods (LSTM, GRU, TCN, RNN, MLP and GBM), and demonstrates the accuracy and parsimony of the EFTS in comparison to the baseline methods and the results previously published in the literature, showing an enhancement greater than 80%. Therefore, EFTS has a great value in high-dimensional time series forecasting in IoE applications.
引用
收藏
页数:12
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