Machine learning approaches for predicting household transportation energy use

Amiri, Shideh Shams; Mostafavi, Nariman; Lee, Earl Rusty; Hoque, Shamia

doi:10.1016/j.cacint.2020.100044

Cited by 14 publications

(1 citation statement)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In blackbox models, such artificial neural networks are less interpretable in terms of the form of the relationship between inputs and output variables. They can outperform other techniques, e.g., the decision trees, predicting energy demand with lower error, but do not provide feature importance insight [11]. Recently, there has been a growing interest in explainable machine learning.…”

Section: Introductionmentioning

confidence: 99%

Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks

Poczęta

Papageorgiou

2022

Energies

View full text Add to dashboard Cite

The aim of this paper is to present a novel approach to energy use forecasting. We propose a nested fuzzy cognitive map in which each concept at a higher level can be decomposed into another fuzzy cognitive map, multilayer perceptron artificial neural network or long short-term memory network. Historical data related to energy consumption are used to construct a nested fuzzy cognitive map in order to better understand energy use behavior. Through the experiments, the usefulness of the nested structure in energy demand prediction is demonstrated, by calculating three popular metrics: Mean Square Error, Mean Absolute Error and the correlation coefficient. A comparative analysis is performed, applying classic multilayer perceptron artificial neural networks, long short-term memory networks and fuzzy cognitive maps. The results confirmed that the proposed approach outperforms the classic methods in terms of prediction accuracy. Moreover, the advantage of the proposed approach is the ability to present complex time series in the form of a clear nested structure presenting the main concepts influencing energy consumption on the first level. The second level allows for more detailed problem analysis and lower forecast errors.

show abstract

Section: Introductionmentioning

confidence: 99%