Energy Consumption Prediction of University Buildings in China and Strategies for Energy Efficiency Management

Qu, Li; Li, Lei; Chen, Wei; Qian, Jin-yuan

doi:10.1115/es2015-49071

Cited by 2 publications

(2 citation statements)

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“…Various studies also found that it is difficult to measure indicators of energy consumption [24], [25]. Targeted energy efficiency strategies on university buildings in China have been developed in accordance with local conditions with different multi-campus and climate factors being among the factors of difficulty in realizing energy efficiency in the buildings [26]. Historical data on daily electricity use in two London South Bank University buildings have been reggregated over normalised data from six input variables to obtain efficient energy use in a building [27].…”

Section: Introductionmentioning

confidence: 99%

“…The ANFIS model used for modeling based on four tested model parameters resulted in root mean square error (RSME) of 0.029817613, with generalized bell membership type and a time of 3 hours and 33 minutes [39]. Electrical energy with GA optimization is used as an ANFIS input parameter to obtain better network performance computing [26], [40]- [42]. Energy usage prediction models include relevance vector machine (RVM), group method data handling (GMDH), ANFIS-biogeography-based optimization (ANFIS-BBO), and ANFIS-improved particle swarm optimization (ANFIS-IPSO) models.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of principal component analysis and ANFIS to improve EEVE Laboratory energy use prediction performance

Desmira

Bakar

Wiryadinata

et al. 2022

IJEECS

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The energy use that is in <span lang="EN-US">excess of practicum students’ needs and the disturbed comfort that the practicum students experience when conducting practicums in the Electrical eengineering vocational education (EEVE) laboratory. The main objective in this study was to figure out how to predict and streamline the use of electrical energy in the EEVE laboratory. The model used to achieve this research’s goal was called the adaptive neuro-fuzzy inference system (ANFIS) model, which was coupled with principal component analysis (PCA) feature selection. The use of PCA in data grouping performance aims to improve the performance of the ANFIS model when predicting energy needs in accordance with the standards set by the campus while still taking students’ confidence in conducting practicum activities during campus operating hours into consideration. After some experiments and tests, very good results were obtained in the training: R=1 in training; minimum RMSE=0.011900; epoch of 100 per iteration; and R=0.37522. In conclusion, the ANFIS model coupled with PCA feature selection was excellent at predicting energy needs in the laboratory while the comfort of the students during practicums in the room remained within consideration.</span>

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of principal component analysis and ANFIS to improve EEVE Laboratory energy use prediction performance

Desmira

Bakar

Wiryadinata

et al. 2022

IJEECS

View full text Add to dashboard Cite

show abstract

Managing the Energy-Efficient Development of the University: Restraints and Ways to Overcome Them

Sotnyk¹,

Sotnyk²,

Olondar³

et al. 2020

Meh. regul. ekon.

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Recently, universities all over the world challenge the problem of reducing energy consumption through the introduction of energy efficient technologies. Higher educational institutions, especially in emerging and developing economies, often face significant barriers to achieving this goal. In developing countries, these obstacles are partly due to technical constraints. However, most of them belong to the organizational, economic, financial, social and educational ones, as is the case in emerging economies. This situation brings up to date the issues of identifying barriers to energy saving on university campuses as well as forming a set of organizational, economic and motivational measures for the energy saving potential implementation in higher educational institutions. On the example of Sumy State University (Ukraine), the study proves a crucial role of the initiative team of energy managers, the necessity of recognition and constant support of the university’s energy efficient development by the topmanagement and wide involvement of teachers and students in energy saving activities. Creating a revolving energy efficiency fund at the university may significantly help to overcome the luck of investment and long-term payback of energy efficient and green energy technologies. In addition, both the promotion of the university’s energy saving achievements by establishing effective communication with all stakeholders and the formation of a system of moral and economic incentives for energy saving essentially contribute to the successful development of energy efficiency policy of the educational institution. The experience of Sumy State University as a case study proves that a systematic approach to the organization of energy saving activities in the university campus based on the continuous improvement of management mechanisms allows achieving the goals of energy efficiency, even with many barriers.

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Energy Consumption Prediction of University Buildings in China and Strategies for Energy Efficiency Management

Cited by 2 publications

References 0 publications

Comparison of principal component analysis and ANFIS to improve EEVE Laboratory energy use prediction performance

Comparison of principal component analysis and ANFIS to improve EEVE Laboratory energy use prediction performance

Managing the Energy-Efficient Development of the University: Restraints and Ways to Overcome Them

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