Identifying Improvement Opportunities in Product Design for Reducing Energy Consumption
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- Perera, H. S. C. & Nagarur, Nagen & Tabucanon, Mario T., 1999. "Component part standardization: A way to reduce the life-cycle costs of products," International Journal of Production Economics, Elsevier, vol. 60(1), pages 109-116, April.
- Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
- Matthew J. Kotchen, 2017. "Longer-Run Evidence on Whether Building Energy Codes Reduce Residential Energy Consumption," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 4(1), pages 135-153.
- Véronique Vasseur & Anne-Francoise Marique & Vladimir Udalov, 2019. "A Conceptual Framework to Understand Households’ Energy Consumption," Energies, MDPI, vol. 12(22), pages 1-22, November.
- Xing, Zongyi & Zhu, Junlin & Zhang, Zhenyu & Qin, Yong & Jia, Limin, 2022. "Energy consumption optimization of tramway operation based on improved PSO algorithm," Energy, Elsevier, vol. 258(C).
- Tomasz Rokicki & Grzegorz Koszela & Luiza Ochnio & Kamil Wojtczuk & Marcin Ratajczak & Hubert Szczepaniuk & Konrad Michalski & Piotr Bórawski & Aneta Bełdycka-Bórawska, 2021. "Diversity and Changes in Energy Consumption by Transport in EU Countries," Energies, MDPI, vol. 14(17), pages 1-21, August.
- Chengdong Li & Zixiang Ding & Dongbin Zhao & Jianqiang Yi & Guiqing Zhang, 2017. "Building Energy Consumption Prediction: An Extreme Deep Learning Approach," Energies, MDPI, vol. 10(10), pages 1-20, October.
- Cavalieri, Sergio & Maccarrone, Paolo & Pinto, Roberto, 2004. "Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry," International Journal of Production Economics, Elsevier, vol. 91(2), pages 165-177, September.
- Wang, Yong & Li, Lin, 2013. "Time-of-use based electricity demand response for sustainable manufacturing systems," Energy, Elsevier, vol. 63(C), pages 233-244.
- Sun-Youn Shin & Han-Gyun Woo, 2022. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms," Energies, MDPI, vol. 15(13), pages 1-20, July.
- Natthanon Phannil & Chaiyan Jettanasen & Atthapol Ngaopitakkul, 2018. "Harmonics and Reduction of Energy Consumption in Lighting Systems by Using LED Lamps," Energies, MDPI, vol. 11(11), pages 1-27, November.
- Acheampong, Alex O. & Dzator, Janet & Dzator, Michael & Salim, Ruhul, 2022. "Unveiling the effect of transport infrastructure and technological innovation on economic growth, energy consumption and CO2 emissions," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
- Garwood, Tom Lloyd & Hughes, Ben Richard & Oates, Michael R. & O’Connor, Dominic & Hughes, Ruby, 2018. "A review of energy simulation tools for the manufacturing sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 895-911.
- Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
- Neves, Sónia Almeida & Marques, António Cardoso & Fuinhas, José Alberto, 2017. "Is energy consumption in the transport sector hampering both economic growth and the reduction of CO2 emissions? A disaggregated energy consumption analysis," Transport Policy, Elsevier, vol. 59(C), pages 64-70.
- Nian Wang & Yingming Zhu, 2022. "The Integration of Traditional Transportation Infrastructure and Informatization Development: How Does It Affect Carbon Emissions?," Energies, MDPI, vol. 15(20), pages 1-23, October.
- Xing, Zongyi & Zhang, Zhenyu & Guo, Jian & Qin, Yong & Jia, Limin, 2023. "Rail train operation energy-saving optimization based on improved brute-force search," Applied Energy, Elsevier, vol. 330(PA).
- Refahi, Amir Hossein & Talkhabi, Hossein, 2015. "Investigating the effective factors on the reduction of energy consumption in residential buildings with green roofs," Renewable Energy, Elsevier, vol. 80(C), pages 595-603.
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- Cosmina-Simona Toader & Ciprian Ioan Rujescu & Andrea Feher & Cosmin Salasan & Lavinia Denisia Cuc & Karoly Bodnar, 2023. "Generation Differences in the Behaviour of Household Consumers in Romania Related to Voluntary Measures to Reduce Electric Energy Consumption," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 25(64), pages 710-710, August.
- Marcin Relich, 2023. "Predictive and Prescriptive Analytics in Identifying Opportunities for Improving Sustainable Manufacturing," Sustainability, MDPI, vol. 15(9), pages 1-14, May.
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Keywords
energy consumption; energy cost; new product development; product design; sustainable development; systems modeling and simulation;All these keywords.
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