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Comparing Generative Adversarial Networks architectures for electricity demand forecasting
https://orcid.org/0000-0001-9108-5801
Abstract This paper introduces short-term load forecasting (STLF) using Generative Adversarial Networks (GAN). STLF was explored using several Artificial Intelligence based methods that offered excellent results. However, the usage of GAN models in this field is very limited, and usually works on creating synthetic load profiles to increase load datasets. This paper investigates the application of GAN for load forecasting by generating daily load profiles. Predicting the daily load is a challenging task that requires accurate and stable models that can capture seasonality and variation in load data. This paper presents a conditional GAN (cGAN) architecture, that uses only four exogenous variables (maximum and minimum temperature, day of the week and month), to predict a daily profile (24 h of the day). Several types of GAN have been compared such as Deep Convolutional GAN, Least Squares GAN and Wasserstein GAN. The generated load profiles were tested on one year of data and compared to the real load profiles. The proposed GAN models provided excellent predictions, averaging a Mean Absolute Percentage Error (MAPE) of 4.99%.
Comparing Generative Adversarial Networks architectures for electricity demand forecasting
https://orcid.org/0000-0001-9108-5801
Abstract This paper introduces short-term load forecasting (STLF) using Generative Adversarial Networks (GAN). STLF was explored using several Artificial Intelligence based methods that offered excellent results. However, the usage of GAN models in this field is very limited, and usually works on creating synthetic load profiles to increase load datasets. This paper investigates the application of GAN for load forecasting by generating daily load profiles. Predicting the daily load is a challenging task that requires accurate and stable models that can capture seasonality and variation in load data. This paper presents a conditional GAN (cGAN) architecture, that uses only four exogenous variables (maximum and minimum temperature, day of the week and month), to predict a daily profile (24 h of the day). Several types of GAN have been compared such as Deep Convolutional GAN, Least Squares GAN and Wasserstein GAN. The generated load profiles were tested on one year of data and compared to the real load profiles. The proposed GAN models provided excellent predictions, averaging a Mean Absolute Percentage Error (MAPE) of 4.99%.
Comparing Generative Adversarial Networks architectures for electricity demand forecasting
https://orcid.org/0000-0001-9108-5801
Abstract This paper introduces short-term load forecasting (STLF) using Generative Adversarial Networks (GAN). STLF was explored using several Artificial Intelligence based methods that offered excellent results. However, the usage of GAN models in this field is very limited, and usually works on creating synthetic load profiles to increase load datasets. This paper investigates the application of GAN for load forecasting by generating daily load profiles. Predicting the daily load is a challenging task that requires accurate and stable models that can capture seasonality and variation in load data. This paper presents a conditional GAN (cGAN) architecture, that uses only four exogenous variables (maximum and minimum temperature, day of the week and month), to predict a daily profile (24 h of the day). Several types of GAN have been compared such as Deep Convolutional GAN, Least Squares GAN and Wasserstein GAN. The generated load profiles were tested on one year of data and compared to the real load profiles. The proposed GAN models provided excellent predictions, averaging a Mean Absolute Percentage Error (MAPE) of 4.99%.
Comparing Generative Adversarial Networks architectures for electricity demand forecasting
Bendaoud, Nadjib Mohamed Mehdi (Autor:in) / Farah, Nadir (Autor:in) / Ben Ahmed, Samir (Autor:in)
Energy and Buildings ; 247
29.05.2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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