A New CNN-Based Method for Short-Term Forecasting of Electrical Energy Consumption in the Covid-19 Period: The Case of Turkey

Abstract

This study proposes a new convolutional neural network (CNN) method with an input-signal decomposition algorithm. With the proposed CNN architecture, hourly electricity consumption data for the Covid-19 period in Turkey were used as input data, and the short-term electricity consumption was forecasted. The input data were decomposed into its subcomponents using a signal decomposition process called Empirical Mode Decomposition (EMD). To extract the deep features, all input data were transformed into 2D feature maps and fed into the CNN. The obtained results were compared with the pre-trained models GoogleNet, AlexNet, SqueezeNet, and ResNet18. Model-wise comparisons showed that the proposed method had the highest correlation coefficient (R) and lowest root mean square error (RMSE) and mean absolute error (MAE) values for 1-h, 2-h, and 3-h. The mean R-values of the proposed method were 95.6%, 95.2%, and 94.0% for 1h, 2h and 3h ahead, respectively. The mean RMSE values were 8.2%, 8.7%, and 10.2% for 1h, 2h and 3h ahead, respectively. The experimental results confirm that the proposed method outperforms other pretrained methods despite its simpler structure.INDEX TERMS Energy consumption, demand forecasting, machine learning, Empirical Mode Decomposition (EMD), Neural networks HIGHLIGHTS  Short-term estimation of electrical energy consumption provides generalized and improved performance.  A new data preprocessing method produces more sophisticated input data.  A deep feature extraction network extracts high-level features.  The proposed model can be integrated into various applications due to its simple, fast, and reliable structure.

Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting

“…Further, most of the researchers have used only one to three models only. For instance, [ 1 ] utilized a multi-input multi-output CNN model to forecast SARS-COV-2 cases in several countries, including Malaysia. Their findings suggested that the CNN model effectively identified local patterns in the data and produced accurate forecasts.…”

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

“…The application of CNNs in the detection, prediction, and analysis of COVID-19 has demonstrated remarkable potential. This section provides a comprehensive literature review of CNNs with a focus on their architecture, applications, challenges, and ethical considerations, specifically in the context of COVID-19 [ 5 , 6 , 10 , 32 , 101 ]. The convolutional layer is the core building block of a CNN and possesses considerable depth and complexity that enables it to learn the spatial hierarchies of features automatically and adaptively.…”

“…Another study used the Bayesian framework to evaluate the impact of lockdown and social distancing on the spread of the pandemic in top-populated countries and found that the pandemic would significantly accelerate if lockdown policies were relaxed. [6,9,[17][18][19][20][21]38,43,45,87] compared the forecasting performance of various deep learning models, including LSTM, CNN, and CNN-LSTM, with traditional machine learning methods, such as SVR and LR. The research indicated that deep learning models, particularly LSTM-CNN, outperformed traditional models in forecasting, with the most precise prediction demonstrated by [33,71,108,144].…”

“…The rapid and widespread spread of COVID-19 has resulted in severe human and financial losses, and has had a significant impact on the healthcare system. There is an urgent need for early prediction of COVID-19 for effective future forecasting and efficient pandemic-tackling policy regulations, reducing strain on the medical care system [1][2][3][4][5][6]. Despite efforts to control the spread of the virus, an increase in the number of patients with viral mutations continues to be a major concern worldwide.…”

“…This study will allow the precise prediction of COVID-19 cases and help the world fight the pandemic by developing efficient strategies for productive future forecasting. In addition, Bayesian optimization was used to test the different models before and after optimization [2][3][4][5][6][7][8][9][10].…”