Study Data from Department of Electrical Engineering Update Knowledge of Electrical Energy Systems (Narx: Contribution-factor-based Short-term Multinodal Load Forecasting for Smart Grid)
- Jan 13, 2021 12:06 pm GMTJan 13, 2021 4:00 pm GMT
- 78 views
2021 JAN 12 (NewsRx) -- By a News Reporter-Staff News Editor at Energy Daily News -- A new study on Energy - Electrical Energy Systems is now available. According to news reporting originating in Bihar, India, by NewsRx journalists, research stated, “A novel multinodal load forecasting method is presented in this paper, which uses smart metered data available from a real-life distribution grid at the NIT Patna campus. Because of the dynamic nature of the load, individual loads need to be predicted simultaneously so that they represent the load of the same instant.”
Financial support for this research came from Science & Engineering Research Board, a statutory body of the Department of Science and Technology (DST), Government of India.
The news reporters obtained a quote from the research from the Department of Electrical Engineering, “The proposed method uses single-stage multinodal forecasting with and without the load contribution factor (LCF), thus reducing the complexity compared to existing two-stage multinodal forecasting methods while improving the forecasting accuracy. It utilizes a nonlinear autoregressive neural network model with exogenous input (NARX-NN), which uses its own predicted output as an input during forecasting; this improves the accuracy of the model and makes it less dependent on external input data compared to other variations of NN. The experimental results show that the proposed method outperforms the existing approaches for multinodal load forecasting of the practical distribution system under consideration.”
According to the news reporters, the research concluded: “Under different input dataset scenarios, the average mean absolute percentage error (MAPE) of the proposed model is 1.44, which represents the best forecasting performance among the competing models.”
This research has been peer-reviewed.
For more information on this research see: Narx: Contribution-factor-based Short-term Multinodal Load Forecasting for Smart Grid. International Transactions on Electrical Energy Systems, 2020. International Transactions on Electrical Energy Systems can be contacted at: Wiley, 111 River St, Hoboken 07030-5774, NJ, USA.
Our news correspondents report that additional information may be obtained by contacting Sneha Rai, Nit Patna, Dept. of Electrical Engineering, Patna 800005, Bihar, India.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1002/2050-7038.12726. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
(Our reports deliver fact-based news of research and discoveries from around the world.)