Load Forecasting in India at Distribution Transformer considering Economic Dynamics

被引:0
作者
Padmanabh, Kumar [1 ]
机构
[1] Bosch Res & Technol Ctr, Bangalore 560095, Karnataka, India
来源
2016 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI) | 2016年
关键词
Demand Response; Forecasting; Gaussian Mixture Model; Smart Grid; DR Event;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The end consumers of Smart Grid have NO say in the ecosystem of electricity Grid. The price of electricity and infrastructure of grid have been solely governed by utility companies and government entities. One of the objectives of the smart grid is to bring consumer on board using loT technologies. Peak demand is a major concern for government and utility. Since different neighborhood would have different consumption pattern and hence load forecasting model at utility level would not predict load at all neighborhood. Socio-economic activities of consumers of a particular neighborhood are coherent. Existing research on this topic considers uniform distribution of assets and uniformity in appliances and pattern of consumption hence they are not good enough for Indian condition. The behavioral pattern of user, other economic activities and different aspect of the weather affect the consumption. In this paper we analyzed the effect of socioeconomic dynamics on demand, developed forecasting mechanism and presented the results. This study reveals that the peak demand is actually growing exponentially. Moreover a unique mechanism of forecasting is presented in this paper which is a two steps process- (i) initially a template pattern of consumption is created using parametric estimation, (ii) then total consumption of the day is created using machine learning technique and (ii) finally total consumption is redistributed to deduce time of the day consumption.
引用
收藏
页码:417 / 423
页数:7
相关论文
共 50 条
  • [1] Research on Load Forecasting Method of Distribution Transformer based on Deep Learning
    Chen, Lei
    Yu, Huihua
    Tong, Li
    Huai, Xu
    Jin, Peipei
    Huang, Yu
    Dou, Chengfeng
    2020 7TH IEEE INTERNATIONAL CONFERENCE ON CYBER SECURITY AND CLOUD COMPUTING (CSCLOUD 2020)/2020 6TH IEEE INTERNATIONAL CONFERENCE ON EDGE COMPUTING AND SCALABLE CLOUD (EDGECOM 2020), 2020, : 228 - 233
  • [2] A new load forecasting model considering planned load shedding effect
    Alkaldy, Esam A. Hashim
    Albaqir, Maythem A.
    Hejazi, Maryam Sadat Akhavan
    INTERNATIONAL JOURNAL OF ENERGY SECTOR MANAGEMENT, 2019, 13 (01) : 149 - 165
  • [3] Transformer training strategies for forecasting multiple load time series
    Hertel M.
    Beichter M.
    Heidrich B.
    Neumann O.
    Schäfer B.
    Mikut R.
    Hagenmeyer V.
    Energy Informatics, 6 (Suppl 1)
  • [4] Distribution Transformer Monitoring for Smart Grid in India
    Singh, Jaswinder
    Aggarwal, Sanjeev
    PROCEEDINGS OF THE FIRST IEEE INTERNATIONAL CONFERENCE ON POWER ELECTRONICS, INTELLIGENT CONTROL AND ENERGY SYSTEMS (ICPEICES 2016), 2016,
  • [5] Optimal Scheduling of the Active Distribution Network with Microgrids Considering Multi-Timescale Source-Load Forecasting
    Lu, Jiangang
    Du, Hongwei
    Zhao, Ruifeng
    Li, Haobin
    Tan, Yonggui
    Guo, Wenxin
    ELECTRONICS, 2024, 13 (17)
  • [6] Data Driven Load Forecasting Method Considering Demand Response
    Luo, Fengzhang
    Yang, Xin
    Wei, Wei
    Lu, Hai
    Zhang, Tianyu
    Shao, Jingpeng
    2020 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM), 2020,
  • [7] SmartFormer: Graph-based transformer model for energy load forecasting
    Saeed, Faisal
    Rehman, Abdul
    Shah, Hasnain Ali
    Diyan, Muhammad
    Chen, Jie
    Kang, Jae-Mo
    SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS, 2025, 73
  • [8] Load Management in Smart Grids Considering Harmonic Distortion and Transformer Derating
    Masoum, M. A. S.
    Moses, P. S.
    Deilami, S.
    2010 INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE (ISGT), 2010,
  • [9] Short-term Load Forecasting of Distribution Transformer Supply Zones Based on Federated Model-Agnostic Meta Learning
    Feng, Changsen
    Shao, Liang
    Wang, Jiaying
    Zhang, Youbing
    Wen, Fushuan
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2025, 40 (01) : 31 - 45
  • [10] A Transformer-Based Method of Multienergy Load Forecasting in Integrated Energy System
    Wang, Chen
    Wang, Ying
    Ding, Zhetong
    Zheng, Tao
    Hu, Jiangyi
    Zhang, Kaifeng
    IEEE TRANSACTIONS ON SMART GRID, 2022, 13 (04) : 2703 - 2714