Development of an Electrical Energy Consumption Model for Malaysian Households, Based on Techno-Socioeconomic Determinant Factors
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- Sheikh Ahmad Zaki & Siti Wan Syahidah & Mohd Fairuz Shahidan & Mardiana Idayu Ahmad & Fitri Yakub & Mohamad Zaki Hassan & Mohd Yusof Md Daud, 2020. "Assessment of Outdoor Air Temperature with Different Shaded Area within an Urban University Campus in Hot-Humid Climate," Sustainability, MDPI, vol. 12(14), pages 1-24, July.
- Jones, Rory V. & Fuertes, Alba & Lomas, Kevin J., 2015. "The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 901-917.
- Vivian W. Y. Tam & Laura Almeida & Khoa Le, 2018. "Energy-Related Occupant Behaviour and Its Implications in Energy Use: A Chronological Review," Sustainability, MDPI, vol. 10(8), pages 1-20, July.
- Beccali, Marco & Ciulla, Giuseppina & Lo Brano, Valerio & Galatioto, Alessandra & Bonomolo, Marina, 2017. "Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy," Energy, Elsevier, vol. 137(C), pages 1201-1218.
- Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
- Sol Kim & Sungwon Jung & Seung-Man Baek, 2019. "A Model for Predicting Energy Usage Pattern Types with Energy Consumption Information According to the Behaviors of Single-Person Households in South Korea," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
- Seunghui Lee & Sungwon Jung & Jaewook Lee, 2019. "Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea," Energies, MDPI, vol. 12(4), pages 1-18, February.
- Boni Sena & Sheikh Ahmad Zaki & Hom Bahadur Rijal & Jorge Alfredo Ardila-Rey & Nelidya Md Yusoff & Fitri Yakub & Mohammad Kholid Ridwan & Firdaus Muhammad-Sukki, 2021. "Determinant Factors of Electricity Consumption for a Malaysian Household Based on a Field Survey," Sustainability, MDPI, vol. 13(2), pages 1-31, January.
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- Surajet Khonjun & Rapeepan Pitakaso & Kanchana Sethanan & Natthapong Nanthasamroeng & Kiatisak Pranet & Chutchai Kaewta & Ponglert Sangkaphet, 2022. "Differential Evolution Algorithm for Optimizing the Energy Usage of Vertical Transportation in an Elevator (VTE), Taking into Consideration Rush Hour Management and COVID-19 Prevention," Sustainability, MDPI, vol. 14(5), pages 1-19, February.
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Keywords
electrical energy consumption model; artificial neural network; socio-demographic; house characteristics; occupant behavior; appliance characteristic;All these keywords.
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