# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
###### Abstract  
The increasing demand for electric vehicles (EVs) requires accurate forecasting to support strategic decisions by manufacturers, policymakers, investors, and infrastructure developers. As EV adoption accelerates due to environmental concerns and technological advances, understanding and predicting this demand becomes critical. In light of these considerations, this study presents an innovative methodology for forecasting EV demand. This model, called EVs-PredNet, is developed using deep learning methods such as LSTM (Long Short-Term Memory) and CNNs (Convolutional Neural Networks). The model comprises convolutional, activation function, max pooling LSTM, and dense layers. Experimental research has investigated four different categories of electric vehicles: battery electric vehicles (BEV), hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), and all electric vehicles (ALL). Performance measures were calculated after conducting experimental studies to assess the model's ability to predict electric vehicle demand. When the performance measures (mean absolute error, root mean square error, mean squared error, R-Squared) of EVs-PredNet and machine learning regression methods are compared, the proposed model is more effective than the other forecasting methods. The experimental results demonstrate the effectiveness of the proposed approach in forecasting the electric vehicle demand. This model is considered to have significant application potential in assessing the adoption and demand of electric vehicles. This study aims to improve the reliability of forecasting future demand in the electric vehicle market and to develop relevant approaches. electric vehicles; forecasting; deep learning; LSTM; CNN +
Footnote †: journal: Applied sciences  
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by fossil fuel vehicles as negative externalities. The environmental degradation resulting from human activities has grown more evident and conspicuous. The Brundtland Report first introduced sustainability in 1987, and over the past two decades, it has emerged as a prominent global concern. Sustainability is a unique issue that has garnered global consensus in efforts to reduce negative externalities. The United Nations has issued sustainable development goals and advised government organizations and business organizations to align their operations with these goals to promote sustainability. The transformation of energy power systems in automotive industries was accelerated by factors like financial crises, oil price fluctuations, emission reduction goals, and energy saving targets. The vision of zero emission benefits and reduced fossil fuel dependency from EVs can contribute to achieving sustainable development goals by enabling sustainable transport [4; 5]. EVs have the potential to reduce air pollution from road vehicles and improve energy security by utilizing renewable energy sources for electrification [6]. In addition to the previously mentioned benefits, one of the primary advantages of electric vehicles is environmental friendliness [7]. Several variables are being considered to influence the increase in electric vehicle sales. These variables were included as independent variables. State-imposed regulations and incentives play a significant role in regulating the EV market [8; 9]. Governments are using practices such as tax incentives and government subsidies to make EVs more attractive, thereby reducing the use of fossil fuels [10]. Governments' efforts to address greenhouse gas emissions and climate change drivers will lead to the introduction of regulations that will increase EV sales [9]. Government subsidies to promote the production of renewable energy and tax regulations to reduce the consumption of fossil fuels also play an important role in shaping the EV market. In this context, total renewable energy production and gasoline and diesel prices are accepted as other independent variables of the research.