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Original Articles

GCC electrical long-term peak load forecasting modeling using ANFIS and MLR methods

&
Pages 269-282 | Received 02 Mar 2018, Accepted 21 Dec 2018, Published online: 26 May 2019

Figures & data

Table 1. Population/Type for the Kingdom of Bahrain (BIeGA, Citation2015).

Figure 1. Bahrain three scenarios of load forecasted (MW) for 2014 – 2024.

Figure 1. Bahrain three scenarios of load forecasted (MW) for 2014 – 2024.

Figure 2. Kuwait three scenarios of the load forecast (MW) for 2014 – 2024.

Figure 2. Kuwait three scenarios of the load forecast (MW) for 2014 – 2024.

Figure 3. KSA three scenarios of the load forecast (MW) for 2014 – 2024.

Figure 3. KSA three scenarios of the load forecast (MW) for 2014 – 2024.

Figure 4. Qatar three scenarios of the load forecast (MW) for 2014 – 2024.

Figure 4. Qatar three scenarios of the load forecast (MW) for 2014 – 2024.

Figure 5. UAE three scenarios of the load forecast (MW) for 2014 – 2024.

Figure 5. UAE three scenarios of the load forecast (MW) for 2014 – 2024.

Figure 6. Oman three scenarios of load forecast (MW) for 2014 – 2024.

Figure 6. Oman three scenarios of load forecast (MW) for 2014 – 2024.

Table 2. Actual Peak Load for GCC Member States (MW).

Table 3. Forecasted Cumulative Growth Rate by GCC Member States.

Figure 7. GCC Surface Sketch for the Output of the Neuro-Fuzzy Models.

Figure 7. GCC Surface Sketch for the Output of the Neuro-Fuzzy Models.