Short Term Load Forecasting using Artificial Neural Network and Time Series Methods: A Case Study of Bishnumati Feeder in Balaju Substation, Nepal
Date
2020-07
Authors
Journal Title
Journal ISSN
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Publisher
Pulchowk Campus
Abstract
Electrical load forecasting takes a crucial role in planning, controlling and operation of
electric power system. The accuracy of actual and forecasted load be very essential for
economically effective control and efficient operation. Short term electric load
forecasting takes an importance role in power system operation and planning of Utilities
Company. Short term load forecasting (STLF) be always one of most critical, sensitive
and accuracy demanding factors of the power systems. An accurate short term load
forecasting improves not only the systems economic viability but also its safety,
stability and reliability. The researcher presented in this works support Artificial
Network and Time Series Methods techniques in forecasting of short term load. This
analysis represents a load forecast using hourly, daily and weekly load of Bishnumati
Feeder of Balaju Substation, by using artificial neural network & time series methods.
Load forecasting be very important and crucial for efficient and necessary operation for
electric power system. This can be taken by obtaining the most effective forecast which
help in minimizing the risk in decision making and changes the prices of operation of
the electric power system. Then the comparison of artificial neural network and time
series method for short term load forecasting that can be used out in this thesis using
actual time electric power in this feeder. Moving average, exponential smoothing are
analyzed in excel and ANN are analyzed in MATLAB software toolbox. The analysis
to be done for the hour-to-hour operation to day to day of the soon be completed of the
Bishnumati feeder. The ANN and time series methods, different analyzing models were
used for forecasting.
For validation of this model it can be used to forecast of an 11kV Bishnumati Feeder
of Nepal for November 10, 2018 and the output is compared to Conveaant University
of Nigeria such as Multilayer feed forward Model. The proposed model of ANN for
Bishnumati Feeder gives weeekly MAPE of 3.67% which is lower than the MAPE
value obtained by the ANN models in Conveant Univesity of 8.37%, but it is within the
limit of acceptance. Again, the value of %MAPE for moving average and exponential
smoothing be 9% & 6.22% in Bishnumati Feeder which is lower than the MAPE value
obtained by the models of Conveant University of 10.3% & 8.31%% but it is within
the limit of acceptance.
Description
Electrical load forecasting takes a crucial role in planning, controlling and operation of
electric power system. The accuracy of actual and forecasted load be very essential for
economically effective control and efficient operation.
Keywords
Citation
MASTER OF SCIENCE IN RENEWABLE ENERGY ENGINEERING