Impact of the Size of Data on the Reliability of Short-Term Load Forecasting Using LSTM and GRU
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DOI:
https://doi.org/10.59287/ijanser.773Keywords:
GRU, LSTM, Short-Term Load Forecasting, Machine LearningAbstract
Load forecasting has been an important aspect of power system operations and with the increase in integration of renewable energy resources in the main grid, the procedure is now more vital than ever. The methods developed to forecast the load of an area have also been improved with the use of artificial intelligence. This study proposes a forecasting training method using Gated Recurrent Units and compares it with the most widely used long-short term memory. The test systems are made of the historical load data from publicly available load data through PJM data miner 2 without the inclusion of weather parameters which reduces the training time of the models along with the reduction in data acquisition cost. The study also considers the impact of predicting future load without access to weather data.
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