## 1 Introduction
The discussion focused on an overview of available prediction techniques, the implemented CNN architecture, the feature selection process, and the performance of the model on a test dataset [23]. [PERSON] used CNN to extract nonlinear relationships between load values. In addition, a load-temperature cube was composed of hourly load and temperature values for a week. Another CNN was trained using the load-temperature cube to learn the hidden nonlinear load-temperature features. Finally, SVR was used for load forecasting [24]. [PERSON] and [PERSON] first encoded univariate time series to images using the Gramian angular fields (GAFs) and Markov transition fields (MTFs) methods. The images were then utilized as inputs for a CNN. This image-based framework pioneered a new class of deep learning algorithms for time series analysis [25; 26]. Since then, many techniques have been introduced to convert time series data into images for use as the inputs of CNNs. The application of a relative position matrix (RPM) time-series encoding approach to convert raw time series into 2D images is investigated to develop an efficient CNN architecture for autonomously learning a higher-level representation of raw time-series data [27]. To feed each CNN, a multi-resolution imaging technique based on Gramian angular fields (GAF) is utilized, allowing for the analysis of diverse time-related periods for a single observation [28]. A 2D representation approach called Relative Position Matrix (RPM) is presented to transform raw time series data to 2D pictures, and an enhanced CNN model is provided to categorize these 2D images. The conversion approach for time series is applied based on the Hue saturation value space, which makes it simple to compare colors since it can transmit the brightness, hue, and vividness of a color very naturally [29]. [PERSON] et al. proposed a new method for predicting solar radiation by encoding time series data into images using Gramian angular fields and convolutional LSTM (ConvLSTM) networks. The preprocessed data become a five-dimensional input tensor that is well-suited to ConvLSTM. The ConvLSTM network uses convolution operations in its input-to-state transition and state-to-state transition [30]. A local phase binary encoding operation was executed to create the histogram of a 2D phase encoding of power signals incorporating neighborhood information, and the suggested encoding method greatly reduced the dimensions of the appliance signals, in addition to improving the discriminating capacity of classifier models [31]. Multivariate time series data were converted from 1D signals to 2D visuals using a variety of encoding approaches, including the Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP) [32]. In order to further leverage the machine learning capabilities of CNNs, more improvements have been developed. The Alexnet algorithm [33], the first deep convolution application, the Inception structure [34], which expands on the classical CNNs, the Resnet strategy [35; 36], which was proposed to solve the problem of the deep convolutional vanishing gradient, and the Resnext method [37], which unifies the concepts of group convolution and residual networks, are all well-known improved forms or variants of CNNs. The transformer model put forth by [PERSON] et al. [38] utilized the attention mechanism to process natural language. The attention mechanism model provides an advantage in the area of natural language processing due to its simplicity and lack of model parameters. The channel attention module and the spatial attention module were proposed by [PERSON] et al. [39] and implemented in the CNN. Each channel serves as a feature detector for the channel attention module.