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Applied Artificial Intelligence
An International Journal
Volume 35, 2021 - Issue 15
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Research Article

Integrating Nonlinear Interval Regression Analysis with a Remnant Grey Prediction Model for Energy Demand Forecasting

Yi-Chung Hua Department of Business Administration, Chung Yuan Christian University, Taoyuan City, TaiwanCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0003-3090-1515View further author information
ORCID Icon,
Yu-Jing Chiua Department of Business Administration, Chung Yuan Christian University, Taoyuan City, TaiwanView further author information
,
Ching-Ying Yub College of Management, Yuan Ze University, Taoyuan City, TaiwanView further author information
&
Jung-Fa Tsaic Department of Business Management, National Taipei University of Technology, Taipei, TaiwanView further author information
Pages 1490-1507 | Received 26 Aug 2020, Accepted 15 Sep 2021, Published online: 21 Sep 2021

Figures & data

Figure 1. A flowchart of the construction of the proposed RGM(1,1)-NIM.

Figure 1. A flowchart of the construction of the proposed RGM(1,1)-NIM.

Table 1. Prediction accuracy obtained by different NIMs for Case I (unit: 100 million kWh)

Table 2. Prediction accuracy obtained by different prediction models for Case I (unit: 100 million kWh)

Figure 2. Lower and upper limits determined by NNs for Case I.

Figure 2. Lower and upper limits determined by NNs for Case I.

Table 3. MAREG obtained by different NIMs for Case I

Figure 3. Lower and upper limits determined by NNs for Case II.

Figure 3. Lower and upper limits determined by NNs for Case II.

Table 4. Prediction accuracy obtained by different NIMs for Case II (unit: 104 kLOE)

Table 5. Prediction accuracy obtained by different prediction models for Case II (unit: 104 kLOE)

Table 6. MAREG obtained by different NIMs for Case II

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