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Session Ⅲ: Real-World AI Applications

Demand Forecasting for Thailand EV Charging Station Based on Deep Learning Techniques

첫 페이지 보기
  • 발행기관
    한국차세대컴퓨팅학회 바로가기
  • 간행물
    한국차세대컴퓨팅학회 학술대회 바로가기
  • 통권
    The 9th International Conference on Next Generation Computing 2023 (2023.12)바로가기
  • 페이지
    pp.99-103
  • 저자
    Kotchakan Fuangngam, Supanan Tantayakul, Arthittaya Narak, Maleerat Maliyeam
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A448127

원문정보

초록

영어
The rapid of electric vehicles (EVs) is challenges and opportunities for energy grid management and infrastructure planning. This research is aims to fill the knowledge gap by employing advanced analytical methods on a 503-day time series dataset from Thailand's EV charging stations. The dataset includes information on date, station name, connector type, energy consumed in kWh, payment in Baht, vehicle brand and model, as well as customer ID. This study focuses on three main objectives: (1) Forecasting daily energy demand with a focus on the top 5 stations in terms of kWh consumption to identify seasonality and trends, (2) Predicting daily revenue based on energy consumption, and (3) Conducting a Geo-Spatial Analysis to recommend optimal locations for installing new EV charging stations. The insights derived are expected to assist in efficient grid management, revenue planning, and strategic infrastructure deployment.

목차

Abstract
I. INTRODUCTION
II. RELATED WORKS
A. Demand prediction of electric Energy and revenue prediction for charging station
B. Location and geo-spatial of charging station
III. METHODLOGY
A. Data Source
B. Data Preparation
C. Well-Formed Format
D. Model Selection
E. Objective of Experiment
IV. EXPERIMENT AND REULT
A. Objective 1: Seasonality and Trends for Efficient Grid Management
B. Objective 2: Predict Daily Revenue (in Baht) Based on Energy Consumption between weekdays and weekends.
C. Objective 3: Geo-Spatial Analysis to Predict Optimal Locations for New Charging Stations Based on Demand.
V. CONCLUSION
REFERENCES

키워드

Electric Vehicle Charging Stations Decision Support System Machine Learning Charging Load Prediction Location-Based Analysis EV Adoption Sustainable Transportation.

저자

  • Kotchakan Fuangngam [ Collage of Information Technology and Digital Innovation King Mongkut's University of Technology North Bangkok,Thailand ]
  • Supanan Tantayakul [ Collage of Information Technology and Digital Innovation King Mongkut's University of Technology North Bangkok,Thailand ]
  • Arthittaya Narak [ Collage of Information Technology and Digital Innovation King Mongkut's University of Technology North Bangkok,Thailand ]
  • Maleerat Maliyeam [ Lecture, Department of innovation technology. King Mongkut's University of Technology North Bangkok,Thailand ]

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국차세대컴퓨팅학회 [Korean Institute of Next Generation Computing]
  • 설립연도
    2005
  • 분야
    공학>컴퓨터학
  • 소개
    본 학회는 차세대 PC 및 그 관련분야의 학술활동을 통하여 차세대 PC의 학문 및 기술발전을 도모하고 산업발전 및 국제협력 증진을 목적으로 한다.

간행물

  • 간행물명
    한국차세대컴퓨팅학회 학술대회
  • 간기
    반년간
  • 수록기간
    2021~2025
  • 십진분류
    KDC 566 DDC 004

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