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Al-Nasiri, Al-bayaty & Al-Hafidh                                                                                                 | 137

                Fig. 9: Load Forecasting Results                  Planning,” J. Inst. Eng. Ser. B, vol. 95, no. 4, pp. 279–285,

                         IV. CONCLUSIONS                          2014.
                                                                [8] E. Akarslan and F. O. Hocaoglu, “A novel short-term
    The research used a new method to predict the weather-
sensitive electrical load by five components. Because of the      load forecasting approach using Adaptive Neuro-Fuzzy
different characteristics and specifications of loads in          Inference System,” Proc. - 2018 6th Int. Istanbul Smart
summer and winter. Mosul city loads have been forecast for        Grids Cities Congr. Fair, ICSG 2018, pp. 160–163, 2018.
a year using Artificial neural network. The results indicate
the accuracy of the prediction model when predicting five       [9] N. Shabbir, R. Amadiahangar, H. A. Raja, L. Kutt, and
load components, the mean absolute percentage error               A. Rosin, “Residential Load Forecasting Using Recurrent
(MAPE) load forecast is equal to (4.02%). We conclude from        Neural Networks,” Proc. - 2020 IEEE 14th Int. Conf.
the research that separating the weather-sensitive load into
Five components increases the prediction accuracy                 Compat. Power Electron. Power Eng. CPE-POWERENG
significantly.                                                    2020, pp. 478–481, 2020.
                                                                [10] G. Weng, C. Pei, J. Ren, H. Jiang, and J. Xu, “Modeling
                     CONFLICT OF INTEREST
                                                                  and Forecasting of Wind Power Output of Urban Regional
     The authors have no conflict of relevant interest to this    Energy Internet Based on Deep Learning,” Journal of
article.
                                                                  Physics: Conference Series, vol. 1732, 2021.
                            REFERENCES                          [11] T. Fujiwara and Y. Ueda, “Load forecasting method for

[1] M. A. Hammad, B. Jereb, B. Rosi, and D. Dragan,               Commercial facilities by determination of working time
  “Methods and Models for Electric Load Forecasting: A            and considering weather information,” 7th Int. IEEE Conf.
  Comprehensive Review,” Logist. Sustain. Transp., vol. 11,       Renew. Energy Res. Appl. ICRERA 2018, vol. 5, pp. 336–
  no. 1, pp. 51–76, 2020.
                                                                  341, 2018.
[2] T. Sun, T. Zhang, Y. Teng, Z. Chen, and J. Fang,            [12] H. L. Imam, M. S. Gaya, and G. S. M. Galadanci, “Short
  “Monthly Electricity Consumption Forecasting Method
  Based on X12 and STL Decomposition Model in an                  term load forecast of Kano zone using artificial intelligent
  Integrated Energy System,” Math. Probl. Eng., vol. 2019,        techniques,” Indones. J. Electr. Eng. Comput. Sci., vol. 16,
  no. 3, 2019.                                                    no. 2, pp. 562–567, 2019.

[3] M. Selim, R. Zhou, W. Feng, and P. Quinsey, “Estimating     [13] Yamama A. I. Al-Nasiri and Majid S. M. Al-Hafidh,
  Energy Forecasting Uncertainty for Reliable AI                  “Three Component Weather-Sensitive load Forecast using
  Autonomous Smart Grid Design,” Energies, vol. 14, no. 1,        Artificial Neural Network,” Al-Rafidain Eng. J., vol. 26,
  p. 247, 2021.                                                   no. 2, pp. 143–149, 2021.

[4] M. Q. Raza, N. Mithulananthan, J. Li, and K. Y. Lee,        [14] K. B. Sahay, S. Sahu, and P. Singh, “Short-term load
  “Multivariate Ensemble Forecast Framework for Demand
  Prediction of Anomalous Days,” IEEE Trans. Sustain.             forecasting of Toronto Canada by using different ANN
  Energy, vol. 11, no. 1, pp. 27–36, 2020.                        algorithms,” 2016 IEEE 6th Int. Conf. Power Syst. ICPS

[5] A. Gupta and P. K. Sarangi, “Electrical load forecasting      2016, 2016.
  using genetic algorithm based back- propagation method,”      [15] S. Nuchprayoon, “Forecasting of daily load curve on
  ARPN J. Eng. Appl. Sci., vol. 7, no. 8, pp. 1017–1020,
  2012.                                                           monthly peak day using load research data and harmonics
                                                                  model,” Proc. - 6th IEEE Int. Conf. Control Syst. Comput.
[6] M. Lekshmi and K. N. Adithya Subramanya, “Short-term          Eng. ICCSCE 2016, no. November, pp. 338–342, 2017.
  load forecasting of 400kV grid substation using R-tool and
  study of influence of ambient temperature on the              [16] W. Yang, L. Qiuyu, C. Qiuna, L. Sijie, Y. Yun, and Y.
  forecasted load,” 2019 2nd Int. Conf. Adv. Comput.              Binjie, “Short-term Load Forecasting Based on Load
  Commun. Paradig. ICACCP 2019, pp. 1–5, 2019.                    Decomposition and Numerical Weather Forecast,”

[7] R. Behera, B. B. Pati, and B. P. Panigrahi, “A Long Term      IEEETrans. Power Syst., 2017.
  Load Forecasting of an Indian Grid for Power System           [17] Majid S. M. Al-Hafidh and G. H. Al-maamary, “Short

                                                                  term electrical load forecasting using holt-winters
                                                                  method,” Al-Rafidain Engineering, vol. 3, no. 3. pp. 2697–

                                                                  2705, 2011.

                                                                [18] A. O. Hoori, A. Al Kazzaz, R. Khimani, Y. Motai, and
                                                                  A. J. Aved, “Electric Load Forecasting Model Using a
                                                                  Multicolumn Deep Neural Networks,” IEEE Trans. Ind.
                                                                  Electron., vol. 67, no. 8, pp. 6473–6482, 2020.

                                                                [19] Majid S. M. Al-Hafidh and M. A. Gasim, “Application

                                                                  of Artificial Neural Networks in Mid-Term Load
                                                                  Forecasting for Residential Sector in Mosul City (Iraq),”

                                                                  in The Second Engineering Conference Golden Jubilee -
                                                                  University of Mosul, vol. 1, pp. 1–10, 2013.
                                                                [20] D. Sonika, S. D. S, and K. Daljeet, “Long Term Load
                                                                  Forecasting Using Soft Computing Techniques”, vol. 6,
                                                                  no. 6, pp. 450–457, 2015.
                                                                [21] F. Li and X. Zhao, “The Application of Genetic
                                                                  Algorithm in Power Short-term Load Forecasting,”

                                                                  IPCSIT, vol. 50, 2012.
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