Residential Electrical Long-term Load Forecast
using Artificial Neural Network: 11/0.415 kV feeder Port Harcourt, Nigeria. DE
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Product details:
- Publisher LAP Lambert Academic Publishing
- Date of Publication 1 January 2024
- Number of Volumes Großformatiges Paperback. Klappenbroschur
- ISBN 9786207486670
- Binding Paperback
- No. of pages52 pages
- Size 220x150 mm
- Language English 525
Categories
Long description:
The essentiality of electric load forecast for the effective design and management of electric power systems has been achieved in this study. PHEDC may plan for infrastructure construction, resource allocation, and energy management by using accurate long-term load forecasts of this study. In the context of the 11/0.415 kV feeder in Port Harcourt, Nigeria, we have discussed the use of ANNs for a long-term of ten (10) years of load forecasting. Curve fitting feed-forward artificial neural network has been used for the simulation on MATLAB 2020 environment, with six input datasets obtained from TCN, and PHEDCs' offices, and average temperature from NIMET-Abuja all in Nigeria from January, 2015-December, 2019. The regression plot of epoch 11 with training; R=1 and validation of 0.9999 has been achieved which indicates how efficient was the training of the dataset. Levenberg-Marquardt algorithm is used as an optimization technique in this study. It shows that ANN provides effective results on long-term electrical load forecasting of the Woji Estate Feeder with a total forecasted value of 29734.4 MWHR and an average value of 24778.67 MWHR at the end of the tenth year.
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