Comparision Of Artificial Recurrent Neural Network For Load Forcasting
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DOI:
https://doi.org/10.52520/masjaps.v7i2id168Keywords:
Electricity load forecasting, LSTM, RNN, LSTM-CNN, smart gridAbstract
With the development of technology and increasing importance of data, the importance and impact of smart grids is increasing day by day. In smart grids, there are devices that help collect data such as senors, smart meters, smart reactive power relays. Electrical power consumption data is one of the data types collected. Load estimation has been a key issue throughout the development of the modern power system. Estimation of energy consumption profile in smart grids is used in the planning of energy supply, estimation of grid maintenance times. Statistical methods, time series method and recently popular artificial neural network method are used in load estimation. In this study, artificial neural network models were used for electrical energy load estimation. The data used in the study were taken from the energy consumption data of the Eastern Kentucky state of the United States. Before the data were given to the artificial neural network they were standardized by the normalization process. Reccurent neural network (RNN), Long short term memory (LSTM) and Convolutional neural network-Long short term mermory (CNN-LSTM), which are Artificial intelligence-based prediction algorithms, were used together for electrical load estimation. State of Kentucky energy consumption data were trained on these models using the Adam optimizer, each with fifty epcoch and loss functions. Kentucky state energy consumption data were trained on these three models, each one 50 acres (epoch) and using the "Adam" optimizer as the loss function, too. The trained models was tried to estimate the electricity consuption energy amounts on the same test set. These models were evaluated by selecting and comparing the estimated data with the actual data by choosing the mean square error and mean absolute error coefficients. As a result of the comparison of the data obtained from the models, it was concluded that the Convolutional Neural Network- Long Short-Term Memory (CNN-LSTM) model gave the least error rate on the the test data compared to the other two models.
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