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Grey and Linear Regression Models in Load Forecasting for Enhanced Smart Grid Management- Smart Grid Modelling | IEEE Conference Publication | IEEE Xplore

Grey and Linear Regression Models in Load Forecasting for Enhanced Smart Grid Management- Smart Grid Modelling


Abstract:

Load forecasting is the estimation of future electric load based on historical electric load data and it is important in power system expansion planning and management. T...Show More

Abstract:

Load forecasting is the estimation of future electric load based on historical electric load data and it is important in power system expansion planning and management. To economically and reliably operate the grid, a power utility company requires accurate demand forecasting models. Therefore, developing and refining the existing forecasting models in the field of load forecasting is an interesting research concept. In this paper we compare advanced Grey Model (GM) with the linear trend regression method in load forecasting for enhancing smart grid management systems. The results show that the Grouped Grey Model, GGM(1,1), which is an improvement from the original Grey Model, GM(1,1), is more accurate and reliable in load forecasting compared with the linear trend regression method. The power utility company will find it useful to adopt the GGM(1,1) in load forecasting for power system planning such as fuel supplies scheduling, maintenance operation and unit management. Hence, smart grid modelling for enhanced grid management.
Date of Conference: 23-27 August 2021
Date Added to IEEE Xplore: 28 September 2021
ISBN Information:
Conference Location: Nairobi, Kenya

Funding Agency:


I. Introduction

A smart grid is an electricity network in which electricity flow is two-way and it involves smart metering, smart appliances, renewable energy resources and energy efficient resources. It can also be defined as an intelligent grid that optimally delivers energy from source point to consumer point. It involves the exchange of information between a utility and its customers. It is a highly interconnected and automated network for making the grid more efficient, reliable, secure and greener. In order for grids to operate in a more economical and reliable way, demand forecasting is important, as it can estimate the amount of power that is likely to be consumed by the load. Thus enabling the electric companies to analyze contingency, reliability, load flows and field scheduling [1].

References

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