Electricity Consumption Time Series Forecasting Using Temporal Convolutional Networks

Torres, José F.; Jiménez-Navarro, M. J.; Martínez‐Álvarez, Francisco; Troncoso, Alicia

doi:10.1007/978-3-030-85713-4_21

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…Three specific ML or DL models have been used to test this methodology. The models are not tested or compared because they have already been widely applied to several time series datasets and have been proved to be accurate and powerful methods for time series forecasting by other authors [41][42][43][44][45][46][47]. The models used to make the predictions are commented as follows.…”

Section: Time Series Forecasting Models Descriptionmentioning

confidence: 99%

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A new approach based on association rules to add explainability to time series forecasting models

et al. 2023

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Section: Time Series Forecasting Models Descriptionmentioning

confidence: 99%

“…Thus, the prediction is the average of the ℎ next values to these past points. • LSTM (Long Short-Term Memory network) [46,47]. It is a deep learning method widely used for time series forecasting.…”

Section: Time Series Forecasting Models Descriptionmentioning

confidence: 99%

A new approach based on association rules to add explainability to time series forecasting models

et al. 2023

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“…Also, convolutional architecture designed for sequential modeling called Temporal Convolutional Network (TCN) is also used for forecast tasks like in [ 29 ], where higher accuracy is obtained. A related literature review shows the application of TCN for electricity load [ 30 ] and electricity price [ 31 ] forecasting. For wind energy applications, TCN is applied to forecast wind power which depends on the wind speed data [ 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

Application of AI for Short-Term PV Generation Forecast

Rocha,

Fiorotti,

Fardin

et al. 2023

Sensors

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The efficient use of the photovoltaic power requires a good estimation of the PV generation. That is why the use of good techniques for forecast is necessary. In this research paper, Long Short-Term Memory, Bidirectional Long Short-Term Memory and the Temporal convolutional network are studied in depth to forecast the photovoltaic power, voltage and efficiency of a 1320 Wp amorphous plant installed in the Technology Support Centre in the University Rey Juan Carlos, Madrid (Spain). The accuracy of these techniques are compared using experimental data along one year, applying 1 timestep or 15 min and 96 step times or 24 h, showing that TCN exhibits outstanding performance, compared with the two other techniques. For instance, it presents better results in all forecast variables and both forecast horizons, achieving an overall Mean Squared Error (MSE) of 0.0024 for 15 min forecasts and 0.0058 for 24 h forecasts. In addition, the sensitivity analyses for the TCN technique is performed and shows that the accuracy is reduced as the forecast horizon increases and that the 6 months of dataset is sufficient to obtain an adequate result with an MSE value of 0.0080 and a coefficient of determination of 0.90 in the worst scenarios (24 h of forecast).

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Prediction of Cancellation Probability of Online Car-Hailing Orders Based on Multi-source Heterogeneous Data Fusion

Sun¹,

Lv²,

Li³

et al. 2022

Wireless Algorithms, Systems, and Applications

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Electricity Consumption Time Series Forecasting Using Temporal Convolutional Networks

Cited by 5 publications

References 29 publications

A new approach based on association rules to add explainability to time series forecasting models

A new approach based on association rules to add explainability to time series forecasting models

Application of AI for Short-Term PV Generation Forecast

Prediction of Cancellation Probability of Online Car-Hailing Orders Based on Multi-source Heterogeneous Data Fusion

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