Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms
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Abstract
Forecasting the electrical load is essential in power system design and growth. It is critical from both a technical and a financial standpoint as it improves the power system performance, reliability, safety, and stability as well as lowers operating costs. The main aim of this paper is to make forecasting models to accurately estimate the electrical load based on the measurements of current electrical loads of the electricity company. The importance of having forecasting models is in predicting the future electrical loads, which will lead to reducing costs and resources, as well as better electric load distribution for electric companies. In this paper, deep learning algorithms are used to forecast the…
Citation impact
339
total citations
- FWCI
- 42.13
- Percentile
- 100%
- References
- 64
Citations per year
Authors
3Topics & keywords
Topics
Keywords
- Recurrent neural network
- Electrical load
- Mean squared error
- Reliability (semiconductor)
- Computer science
- Electric power system
- Electricity
- Stability (learning theory)
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