Comparison of Electricity Load Prediction Errors Between Long Short-Term Memory Architecture and Artificial Neural Network on Smart Meter Consumer
Brain; Errors; Forecasting; Learning algorithms; Mean square error; Memory architecture; Network architecture; Smart meters; Time series; Demand-side; Electricity load; Error values; Load predictions; Machine learning algorithms; Mean absolute error; Mean squared error; Prediction errors; Regression...
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Springer Science and Business Media Deutschland GmbH
2023
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my.uniten.dspace-264522023-05-29T17:10:41Z Comparison of Electricity Load Prediction Errors Between Long Short-Term Memory Architecture and Artificial Neural Network on Smart Meter Consumer Salleh N.S.M. Suliman A. J�rgensen B.N. 54946009300 25825739000 7202434812 Brain; Errors; Forecasting; Learning algorithms; Mean square error; Memory architecture; Network architecture; Smart meters; Time series; Demand-side; Electricity load; Error values; Load predictions; Machine learning algorithms; Mean absolute error; Mean squared error; Prediction errors; Regression problem; Times series; Long short-term memory Machine learning can perform electricity load prediction on the demand side. This paper compared the electricity prediction errors between two machine learning algorithms: Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) architecture. LSTM can solve the regression problem in time-series. Due to that, this paper applied LSTM. The traditional machine learning approach, ANN, was used to compare the effectiveness of LSTM in performing the time-series prediction. A dataset that consisted of historical electricity consumption data with independent variables was used in this study. The mean squared error (MSE) and mean absolute error (MAE) evaluation metrics were used to evaluate the models. The model generated using LSTM showed the lowest error with MSE value of 0.1238 and MAE value of 0.0388. These results indicated that choosing a suitable machine learning algorithm for the time-series problem could improve the model generated from the training session. � 2021, Springer Nature Switzerland AG. Final 2023-05-29T09:10:41Z 2023-05-29T09:10:41Z 2021 Conference Paper 10.1007/978-3-030-90235-3_52 2-s2.0-85120530402 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120530402&doi=10.1007%2f978-3-030-90235-3_52&partnerID=40&md5=2a74707944e6202837cf4a16a2d5dc60 https://irepository.uniten.edu.my/handle/123456789/26452 13051 LNCS 600 609 Springer Science and Business Media Deutschland GmbH Scopus |
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Brain; Errors; Forecasting; Learning algorithms; Mean square error; Memory architecture; Network architecture; Smart meters; Time series; Demand-side; Electricity load; Error values; Load predictions; Machine learning algorithms; Mean absolute error; Mean squared error; Prediction errors; Regression problem; Times series; Long short-term memory |
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54946009300 |
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54946009300 Salleh N.S.M. Suliman A. J�rgensen B.N. |
| format |
Conference Paper |
| author |
Salleh N.S.M. Suliman A. J�rgensen B.N. |
| spellingShingle |
Salleh N.S.M. Suliman A. J�rgensen B.N. Comparison of Electricity Load Prediction Errors Between Long Short-Term Memory Architecture and Artificial Neural Network on Smart Meter Consumer |
| author_sort |
Salleh N.S.M. |
| title |
Comparison of Electricity Load Prediction Errors Between Long Short-Term Memory Architecture and Artificial Neural Network on Smart Meter Consumer |
| title_short |
Comparison of Electricity Load Prediction Errors Between Long Short-Term Memory Architecture and Artificial Neural Network on Smart Meter Consumer |
| title_full |
Comparison of Electricity Load Prediction Errors Between Long Short-Term Memory Architecture and Artificial Neural Network on Smart Meter Consumer |
| title_fullStr |
Comparison of Electricity Load Prediction Errors Between Long Short-Term Memory Architecture and Artificial Neural Network on Smart Meter Consumer |
| title_full_unstemmed |
Comparison of Electricity Load Prediction Errors Between Long Short-Term Memory Architecture and Artificial Neural Network on Smart Meter Consumer |
| title_sort |
comparison of electricity load prediction errors between long short-term memory architecture and artificial neural network on smart meter consumer |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2023 |
| _version_ |
1806423299667787776 |
| score |
13.222552 |