Prediction of electricity sales using neural based inverse distance weighting method

  • Authors

    • Bustani .
    • Sunu Pradana
    • Mulyanto .
    • Nurjanana .
    2018-03-05
    https://doi.org/10.14419/ijet.v7i2.2.12735
  • connected power, electricity sales, SOM algorithm, IDW method, ANN-BP

  • Abstract

    Prediction of electricity sales becomes important for State Electricity Company of Indonesia (PLN) in order to estimate the Statement of Profit and Loss in next year. To obtain good predictive results may require many variables and data availability. There are many available methods that do not require so many variables to get predicted results with a good approximation. The aim of this study was to predict electricity sales by using an interpolation method called IDW (Inverse Distance Weighting). Several data samples are mapped into Cartesian coordinates. The data samples used are power connected to the household (X-axis), to industry (Y-axis), and electricity sales (Z value). Firstly, the sampled data clustered by using SOM algorithm. The Z value in each cluster is predicted by using the IDW method. The prediction results of IDW method are then optimized using ANN-BP (Artificial Neural Network Back Propagation). The trained net structure is then used to predict the electricity sale in next year.

     

     

  • References

    1. [1] G. Anosh and E. P. Mishra, "Time series analysis model to forecast rainfall for Allahabad region," Journal of Pharmacognosy and Phytochemistry, vol. 6, pp. 1418-1421, (2017).

      [2] J. Byun, Y. Han, I. P. Gorlov, J. A. Busam, M. F. Seldin, and C. I. Amos, "Ancestry inference using principal component analysis and spatial analysis: a distance-based analysis to account for population substructure," BMC Genomics, vol. 18, (2017).

      [3] S. Saeed, L. Hussain1, I. A. Awan, and A. Idris, "Comparative Analysis of different Statistical Methods for Prediction of PM2.5 and PM10 Concentrations in Advance for Several Hours," IJCSNS International Journal of Computer Science and Network Security, vol. 17, (2017).

      [4] P. K. Sahu and R. Shrivastava, "Prediction of Tool Life based on Empirical Mode Decomposition and Gaussian Process Regression," IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), vol. 14, pp. 52-58, (2017).

      [5] K. H. Tan, T. Logenthiran, and W. L. Woo, "Forecasting of wind energy generation using Self-Organizing Maps and Extreme Learning Machines," pp. 451-454, 2016.

      [6] A. M. Al-saadi, S. Kh. Zamiem, L. A. A. Al-Jumaili, M. JameelJubair, and H. Abdalla Al- Hashemi, "Estimating the Optimum Duration of Road Projects Using Neural Network Model," International Journal of Engineering and Technology, vol. 9, pp. 3458-3469, (2017).

      [7] T. R. Neelakantan, S. Ramasundaram, and R. Vinoth, "Prediction of Concrete Strength Using Microwave Based Accelerated Curing Parameters by Neural Network," International Journal of Engineering and Technology (IJET), vol. 5, pp. 157-164, (2013).

      [8] G. Saha, K. Chakraborty, and P. Das, "Probabilistic Neural Network Based Voltage Stability Monitoring of Electrical Transmission Network in Energy Management Scenario," International Journal of Engineering and Technology (IJET), vol. 9, pp. 4434-4442, (2017).

      [9] M. Khairalla, Xu-Ning, and N. T. AL-Jallad, "Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods," (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 8, pp. 319-327, (2017).

      [10] A. Malav, K. Kadam, and P. Kamat, "PREDICTION OF HEART DISEASE USING K-MEANS and ARTIFICIAL NEURAL NETWORK as HYBRID APPROACH to IMPROVE ACCURACY," International Journal of Engineering and Technology (IJET), vol. 9, pp. 3081-3085, (2017).

      [11] E. Oktavia, Widyawan, and I. W. Mustika, "Inverse Distance Weighting and Kriging Spatial Interpolation for Data Center Thermal Monitoring," presented at the 2016 1st International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, (2016).

      [12] J. Batista Mattos, Silva, Kaique Brito, J. M. Lima, and L. A. Oliveira, "Zoning of the use and quality of groundwater as a subsidy for the management of water resources: The case of the urban area of the municipality of Lençóis, Bahia, Northeastern Brazil," Journal of Hyperspectral Remote Sensing, vol. 7, pp. 40-49, (2017).

      [13] V. Kishorebabu, G. Packyanathan, H. Kamatham, and V. Shankar, "An adaptive decision based interpolation scheme for the removal of high density salt and pepper noise in images," EURASIP Journal on Image and Video Processing, vol. 2017, (2017).

      [14] PLN, "The Statistics of National Electrical Company of Indonesia Year 2010," S. E. C. o. Indonesia, Ed., ed. Jakarta: Center of State Electrical Company of Indonesia, (2010).

      [15] PLN, "The Statistics of National Electrical Company of Indonesia Year 2011," S. E. C. o. Indonesia, Ed., ed: Center of State Electrical Company of Indonesia, (2011).

      [16] PLN, "The Statistics of National Electrical Company of Indonesia Year 2012," S. E. C. o. Indonesia, Ed., ed: Center of State Electrical Company of Indonesia, (2012).

      [17] PLN, "The Statistics of National Electrical Company of Indonesia Year 2013," S. E. C. o. Indonesia, Ed., ed: Center of State Electrical Company of Indonesia, (2013).

      [18] PLN, "The Statistics of National Electrical Company of Indonesia Year 2014," S. E. C. o. Indonesia, Ed., ed: Center of State Electrical Company of Indonesia, (2014).

      [19] PLN, "The Statistics of National Electrical Company of Indonesia Year 2015," S. E. C. o. Indonesia, Ed., ed: Center of State Electrical Company of Indonesia, (2015).

      [20] PLN, "The Statistics of National Electrical Company of Indonesia Year 2016," S. E. C. o. Indonesia, Ed., ed: Center of State Electrical Company of Indonesia, (2016).

      [21] M. H. Beale and M. T. Hagan. ((2015)). Neural Network ToolboxTM MATLAB R2015a – User’s Guide.

      [22] Haviluddin, A. Yunianta, A. H. Kridalaksana, Z. Arifin, B. Kresnapati, F. Rahman, A. F. O. Gaffar, H. Y. Irawan, M. Mulyo, and A. Pranolo, "Modelling of Network Traffic Usage Using Self-Organizing Maps Techniques," in 2016 2nd International Conference on Science in Information Technology (ICSITech), 2016, pp. 334-338.

      [23] Haviluddin and R. Alfred, "A Genetic-Based Backpropagation Neural Network for Forecasting in Time-Series Data," in The 2015 International Conference on Science in Information Technology (ICSITech 2015), Yogyakarta, Indonesia, 2015, pp. xxx-xxx.

  • Downloads

  • How to Cite

    ., B., Pradana, S., ., M., & ., N. (2018). Prediction of electricity sales using neural based inverse distance weighting method. International Journal of Engineering & Technology, 7(2.2), 65-69. https://doi.org/10.14419/ijet.v7i2.2.12735

    Received date: 2018-05-12

    Accepted date: 2018-05-12

    Published date: 2018-03-05