Load forecasting in India at distribution transformer considering economic dynamics
- Resource Type
- Conference
- Authors
- Padmanabh, Kumar
- Source
- 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI) Advances in Computing, Communications and Informatics (ICACCI), 2016 International Conference on. :417-423 Sep, 2016
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Economic indicators
Forecasting
Load forecasting
Government
Microeconomics
Companies
Demand Response
Gaussian Mixture Model
Smart Grid
DR Event
- Language
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 IoT 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.