# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
###### Abstract
The study uses the monthly real gross domestic product index to represent the average income of potential buyers. [PERSON], [PERSON], and [PERSON] (2017) discovered a correlation between income and the degree of concern for environmental issues in their research. As income levels rise, consumers are becoming more environmentally conscious, leading to an increasing preference for electric vehicles among potential car buyers [11]. [PERSON], [PERSON], and [PERSON] (2019) found that 49% of electric car buyers in California belonged to the high-income group in their study on the characteristics of these consumers [12]. Consumers can buy new vehicles using either their savings or credit. [PERSON] et al. (2016) included credit rates and income as factors that influence the decision to buy a new vehicle [13]. The study also included the new auto loan rates as a variable. Consumers' preferences for purchasing electric vehicles can be influenced by the availability of an adequate number of charging stations [14; 15]. In 2016, there were 200,000 electric vehicle charging points, which rose to 1.7 million by 2022. The projected figure is 3.32 million by 2028, according to Statista's 2023 report. [PERSON] et al. (2024) stated that those who consider purchasing electric vehicles are influenced by five different factors: economic, environmental, performance, psychological, and barriers. The study placed significant emphasis on charging stations as one of the performance factors [16]. In this context, another independent variable used in the study was determined as the number of electric vehicle charging stations. Electric vehicles are more preferable due to the decreased cost of energy compared to fossil fuels, as stated by [PERSON], [PERSON], and [PERSON] in 2019 [17]. The study also includes electricity as a variable, as it serves as the primary fuel source for EVs. This research also includes the consumer and producer price index, which influences electric vehicle sales [18]. Competition is increasing in the market, involving both traditional vehicle producers and new manufacturers specializing in electric vehicles. The International Energy Agency predicts that the quantity of electric car models will increase twofold in 2022 compared to 2018, reaching a total of 500 [1]. The rising demand for electric automobiles attracts electric car producers. Researchers and practitioners find the issue of electric vehicles intriguing, leading to an increasing demand for in-depth study in this area. Recently, there has been a rise in studies in the literature focusing on projecting the demand for electric vehicles. The expected development of the electric vehicle market in the coming years is expected to have an impact on various industry players, including original equipment manufacturers due to the multiplier effect, producers of sustainable energy sources who may see a decrease in demand for fossil fuels, customers who may gradually demand different models, and used car dealers. For these reasons, an accurate estimation of the demand for electric vehicles will enable forecasts to be made and production to be adapted to the market. In this paper, we proposed an approach based on deep learning approaches including LSTM (Long Short-Term Memory) and CNNs (Convolutional Neural Networks) to forecast the quantity of battery electric vehicles (BEV), plug-in hybrid electric vehicles (PHEV), and hybrid electric vehicles (HEV). This could improve the accuracy and efficiency of electric vehicle demand forecasting. The proposed EVs-PredNet model works with four different outputs: BEV, HEV, PHEV, and ALL. This allows the individual estimation of the demand for different categories of electric vehicles. The proposed EVs-PredNet model demonstrates superior performance compared to machine learning regression models.