Prediction of energy consumption for manufacturing small and medium-sized enterprises (SMEs) considering industry characteristics
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DOI: 10.1016/j.energy.2024.131621
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- Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
- Dwivedi, Yogesh K. & Hughes, Laurie & Kar, Arpan Kumar & Baabdullah, Abdullah M. & Grover, Purva & Abbas, Roba & Andreini, Daniela & Abumoghli, Iyad & Barlette, Yves & Bunker, Deborah & Chandra Kruse,, 2022.
"Climate change and COP26: Are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action,"
International Journal of Information Management, Elsevier, vol. 63(C).
- Yogesh K Dwivedi & Laurie Hughes & Arpan Kumar Kar & Abdullah M Baabdullah & Purva Grover & Roba Abbas & Daniela Andreini & Iyad Abumoghli & Yves Barlette & Deborah Bunker & Leona Chandra Kruse & Ioan, 2021. "Climate change and COP26: Are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action," Post-Print hal-04295011, HAL.
- Lambert, Philippe, 2021. "Moment-based density and risk estimation from grouped summary statistics," LIDAM Discussion Papers ISBA 2021039, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
- He, Yan & Wu, Pengcheng & Li, Yufeng & Wang, Yulin & Tao, Fei & Wang, Yan, 2020. "A generic energy prediction model of machine tools using deep learning algorithms," Applied Energy, Elsevier, vol. 275(C).
- Kapp, Sean & Choi, Jun-Ki & Hong, Taehoon, 2023. "Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
- Phylipsen, G. J. M. & Blok, K. & Worrell, E., 1997. "International comparisons of energy efficiency-Methodologies for the manufacturing industry," Energy Policy, Elsevier, vol. 25(7-9), pages 715-725.
- Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
- Xiong, Ping-ping & Dang, Yao-guo & Yao, Tian-xiang & Wang, Zheng-xin, 2014. "Optimal modeling and forecasting of the energy consumption and production in China," Energy, Elsevier, vol. 77(C), pages 623-634.
- Maaouane, Mohamed & Zouggar, Smail & Krajačić, Goran & Zahboune, Hassan, 2021. "Modelling industry energy demand using multiple linear regression analysis based on consumed quantity of goods," Energy, Elsevier, vol. 225(C).
- Hyndman, Rob J. & Koehler, Anne B., 2006.
"Another look at measures of forecast accuracy,"
International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
- Rob J. Hyndman & Anne B. Koehler, 2005. "Another Look at Measures of Forecast Accuracy," Monash Econometrics and Business Statistics Working Papers 13/05, Monash University, Department of Econometrics and Business Statistics.
- Hyungah Lee & Dongju Kim & Jae-Hoi Gu, 2023. "Prediction of Food Factory Energy Consumption Using MLP and SVR Algorithms," Energies, MDPI, vol. 16(3), pages 1-21, February.
- Bhattacharyya, Subhes C. & Timilsina, Govinda R., 2009. "Energy demand models for policy formulation : a comparative study of energy demand models," Policy Research Working Paper Series 4866, The World Bank.
- Duan, Huiming & Pang, Xinyu, 2021. "A multivariate grey prediction model based on energy logistic equation and its application in energy prediction in China," Energy, Elsevier, vol. 229(C).
- Hao, Xiaochen & Guo, Tongtong & Huang, Gaolu & Shi, Xin & Zhao, Yantao & Yang, Yue, 2020. "Energy consumption prediction in cement calcination process: A method of deep belief network with sliding window," Energy, Elsevier, vol. 207(C).
- Fais, Birgit & Sabio, Nagore & Strachan, Neil, 2016. "The critical role of the industrial sector in reaching long-term emission reduction, energy efficiency and renewable targets," Applied Energy, Elsevier, vol. 162(C), pages 699-712.
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