Application of Seasonal and Multivariable Grey Prediction Models for Short-Term Load Forecasting

Tuncay Özcan

Alphanumeric Journal, 2017, vol. 5, issue 2, 329-338

Abstract: Short-term electricity load forecasting is one of the most important operations in electricity markets. The success in the operations of electricity market participants partially depends on the accuracy of load forecasts. In this paper, three grey prediction models, which are seasonal grey model (SGM), multivariable grey model (GM (1,N)) and genetic algorithm based multivariable grey model (GAGM (1,N)), are proposed for short-term load forecasting problem in day-ahead market. The effectiveness of these models is illustrated with two real-world data sets. Numerical results show that the genetic algorithm based multivariable grey model (GAGM (1,N)) is the most efficient grey forecasting model through its better forecast accuracy.

Keywords: Genetic Algorithm; Grey Prediction; Parameter Optimization; Short Term Load Forecasting (search for similar items in EconPapers)
JEL-codes: C44 C53 C63 (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:anm:alpnmr:v:5:y:2017:i:2:p:329-338

DOI: 10.17093/alphanumeric.359942

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