Peeking inside the black-box: Explainable machine learning applied to household transportation energy consumption

SS Amiri, S Mottahedi, ER Lee, S Hoque - Computers, Environment and …, 2021 - Elsevier
Sustainability policies to mitigate transportation energy impacts on the urban environment
are urgently needed. Energy prediction models provide critical information to decision …

[HTML][HTML] An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector

Q Qiao, H Eskandari, H Saadatmand, MA Sahraei - Energy, 2024 - Elsevier
The transportation sector is deemed one of the primary sources of energy consumption and
greenhouse gases throughout the world. To realise and design sustainable transport, it is …

Prediction of transportation energy demand by novel hybrid meta-heuristic ANN

MA Sahraei, MK Çodur - Energy, 2022 - Elsevier
Road automobiles are deemed one of the major resources of energy consumption
throughout cities. To realize and design sustainable urban transport, it is essential to …

A review on the applicability of machine learning techniques to the metamodeling of energy systems

AR Starke, AK da Silva - Numerical Heat Transfer, Part B …, 2023 - Taylor & Francis
The use of physics-based models for the development and optimization of energy systems is
popular due to their versatility. However, their inherent complexity often makes these …

How do machines predict energy use? Comparing machine learning approaches for modeling household energy demand in the United States

JW Burnett, LL Kiesling - Energy Research & Social Science, 2022 - Elsevier
This paper illustrates the use of different machine learning techniques to estimate household
energy demand. To demonstrate the performance of the techniques, we discuss how the …

SA-LSTMs: A new advance prediction method of energy consumption in cement raw materials grinding system

G Liu, K Wang, X Hao, Z Zhang, Y Zhao, Q Xu - Energy, 2022 - Elsevier
Electricity consumption is a major energy efficiency indicator in cement raw materials
grinding system. Advance prediction of electricity consumption provides the basis for cement …

Energy use forecasting with the use of a nested structure based on fuzzy cognitive maps and artificial neural networks

K Poczeta, EI Papageorgiou - Energies, 2022 - mdpi.com
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 …

Assessing the factors affecting the perceived crossing speed of pedestrians and investigating the direct and indirect effects of crash risk perception on perceived …

A Saxena - Journal of Transport & Health, 2023 - Elsevier
Walking is the primary means of transportation. For assessing individual's health, travel
behaviour and benchmarking service levels of pedestrian infrastructure, walking/crossing …

A method for short-term passenger flow prediction in urban rail transit based on deep learning

N Dong, T Li, T Liu, R Tu, F Lin, H Liu, Y Bo - Multimedia Tools and …, 2024 - Springer
Short-term passenger flow prediction is a critical component of urban rail transit operations.
However, predictions of passenger flow are mostly focused on one station, and land use …

An adaptive search equation-based artificial bee colony algorithm for transportation energy demand forecasting

D ÖZDEMİR, S Dörterler - Turkish Journal of Electrical …, 2022 - journals.tubitak.gov.tr
This study aimed to develop a new adaptive artificial bee colony (A-ABC) algorithm that can
adaptively select an appropriate search equation to more accurately estimate transport …