Short Term Electric Load Forecasting of Kathmandu Valley of Nepal using Artificial Neural Network

  • Binod Bhandari Department of Mechanical Engineering, Institute of Engineering, Tribhuvan University, Nepal
  • Shree Raj Shakya Department of Mechanical Engineering, Institute of Engineering, Tribhuvan University, Nepal
  • Ajay Kumar Jha Department of Mechanical Engineering, Institute of Engineering, Tribhuvan University, Nepal

Abstract

Decision making in the energy sector has to be based on accurate forecasts of the load demand. Short-term forecasting, which forms the focus of this paper, gives a day ahead hourly forecast of electric load. This forecast can help to make important decisions in the field of scheduling, contingency analysis, load flow analysis, preventing imbalance in the power generation and load demand, load switching strategies, thus leading to greater network reliability and power quality. A method called Artificial Neural Network is used to anticipate the future load of Kathmandu Valley of Nepal. The Neural Network is build, trained with historical data along with seven different input variables and used for prediction of day ahead 24 hours load. The output is validated with the real Load collected from NEA. In addition, forecasting is performed by some other time series methods as well, and whose output are compared with that of neural network. The range of Mean Absolute Deviation for four different time series models lied between 1.50-2.59. When the errors were calculated in terms of MSE and MAPE the range of these values were found to be in between 2.59-7.78, and 1.61- 5.07 respectively. The Artificial Neural Network proved to be the more accurate forecast method when the results are compared in terms of error measurements with a MAD having 1.23, MSE having 1.79 and MAPE having 1.17. The Neural Network proved to be more accurate method comparatively with satisfactory minimum error.

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Author Biographies

Binod Bhandari, Department of Mechanical Engineering, Institute of Engineering, Tribhuvan University, Nepal

Binod Bhandari is an Electrical engineer. He received Bachelor’s Degree in Electrical Engineering from Pulchowk Campus, Institute of Engineering, Tribhuvan University, Nepal. He is graduate student of Master of Science in Renewable Energy Engineering in same Institution. At present, he is working as a full time lecturer in the Department of Electrical Engineering at Kathmandu Engineering College, Kathmandu. He has published few papers in national journals related to electrical engineering. He is also the founder chairman of non-government organization Nexus UNESCO Club.His research interests lie in the area of Renewable Energy Engineering, Power System and Social Science.

Shree Raj Shakya, Department of Mechanical Engineering, Institute of Engineering, Tribhuvan University, Nepal

Shree Raj Shakya is Director of Center for Energy Studies and coordinator of MS in Energy System Planning and Management, Institute of Engineering, Tribhuvan University, Nepal. He has received PhD Degree in Energy Engineering from Asian Institute of Technology, Thailand. He completed M.Sc. in Renewable Energy Engineering from Institute of Engineering, Pulchowk Campus. He has published more than 30 papers in different international and national peerreviewed journals. His fields of research are energy systems modeling, analysis and planning, energy and climate change policies, low carbon and sustainable development, green growth and renewable energy technologies.

Ajay Kumar Jha, Department of Mechanical Engineering, Institute of Engineering, Tribhuvan University, Nepal

Ajay Kumar Jha is coordinator of M.Sc. in Renewable Energy Engineering, Institute of Engineering, Pulchowk Campus. He has received his PhD degree in Environmental Science and Engineering from Harbin Institute of Engineering, China. He completed M.Sc. in Renewable Energy Engineering and Bachelors in Mechanical Engineering from Institute of Engineering, Pulchowk Campus. He has published more than forty research papers in national and international journals. His fields of research are Renewable Energy, Solar PV Technologies, Biogas Technology.

Published
2018-12-14
How to Cite
Bhandari, B., Shakya, S., & Jha, A. (2018). Short Term Electric Load Forecasting of Kathmandu Valley of Nepal using Artificial Neural Network. Kathford Journal of Engineering and Management, 1(1), 43-48. https://doi.org/10.3126/kjem.v1i1.22022
Section
Articles