Analysis of Electric Load Forecasting using Artificial Intelligence

Authors

  • Srashti Shrivastava DDIPG Scholar, Department of Electrical & Electronics Engineering, UTD RGPV, Bhopal India
  • Dr. Krishna Teerth Chaturvedi Professor, Department of Electrical & Electronics Engineering, UTD RGPV, Bhopal, India

DOI:

https://doi.org/10.24113/ijoscience.v4i5.137

Abstract

Electricity demand forecasts are extremely important for energy suppliers and other actors in production, transmission, distribution and energy markets. Accurate models for predicting the load of electricity are critical to the operation and planning of a service company. load forecasts are extremely important for energy suppliers and other actors in production, transmission, distribution and energy markets. short-term load forecasts play an important role in the operation of power systems to ensure an immediate balance between energy production and demand. The accuracy of the prediction generated by the artificial intelligence has several factors, including, but not limited to, what is used to form the network algorithm, how much and which type of data are used in the network’s training set. In this research the best combination has been investigated of these factors to decrease performance parameters to give the best forecast possible.

Artificial intelligence techniques have gained importance in reducing estimation errors. Artificial neural network, Extreme Learning Machine and Decision tree such as LSBOOST and RF are among these artificial intelligence techniques. That are used in this research work for performance analysis. In this work, performance evaluation metrics such as MSE, RMSE, MAE and MAPE values are analysed and it is concluded that Random forest decision tree forecasting algorithm gives better performance of forecasting as compared to other artificial intelligence algorithms for 24 hours load forecasting as well as for 7 day load forecasting.

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References

Hippert, H. S., Pedreira, C. E., & Souza, R. C.,” Neural networks for short-term load forecasting: A review and evaluation”, IEEE Transactions on Power Systems, 16, 44–55, 2001.

Alfares, H. K., & Nazeeruddin, M., “Electric load forecasting: Literature survey and classification of methods”, International Journal of Systems Science, 33, 23–34, 2002.

Metaxiotis, K., Kagiannas, A., Askounis, D., & Psarras, J., “ Artificial intelligence in short term electric load forecasting: a state-of-theart survey for the researcher”, Energy Conversion and Management, 44, 1525–1534, 2003.

Martin Längkvist, Lars Karlsson, Amy Loutfi, A review of unsupervised feature learning and deep learning for time-series modeling, Pattern Recognition Letters, Volume 42, 1 June 2014, Pages 11-24.

A. Baliyan, K. Gaurav, and S. K. Mishra, “A review of short term load forecasting using artificial neural network models,” Procedia Computer Science, vol. 48, pp. 121-125, 2015.

Xiaoqin Wu, Zhixi Shen, and Yongduan Song, “A Novel Approach for Short-Term Electric Load Forecasting”, IEEE, 2016.

Jian Zheng, Cencen Xu, Ziang Zhang and Xiaohua Li, “Electric Load Forecasting in Smart Grids Using Long-Short-Term-Memory based Recurrent Neural Network”, IEEE, 2017.

Toma´s Vantuch, Michal Pr´?lepok, “An Ensemble of Multi-objective Optimized Fuzzy Regression Models for Short-term Electric Load Forecasting”, IEEE, 2017.

Junran Peng, Shengyu Gao, Anzi Ding, “Study of the Short-Term Electric Load Forecast Based on ANFIS”, IEEE, 2017.

Mitchell Easley et al. “Deep Neural Networks for Short-Term Load Forecasting in ERCOT System” IEEE, 2018.

Shady Mahmoud Elgarhy et al. “Short Term Load Forecasting Using ANN Technique” IEEE, 2017.

Jian Zheng et al. “Electric Load Forecasting in Smart Grids Using Long-Short-Term-Memory based Recurrent Neural Network”, IEEE, 2017.

Runliai Jiao et al. “A Model Combining Stacked Auto Encoder and Back Propagation Algorithm for Short-term Wind Power Forecasting” IEEE, 2018.

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Published

2018-05-26

How to Cite

Shrivastava, S., & Chaturvedi, D. K. T. (2018). Analysis of Electric Load Forecasting using Artificial Intelligence. SMART MOVES JOURNAL IJOSCIENCE, 4(5), 15-21. https://doi.org/10.24113/ijoscience.v4i5.137