Abstract:
In price directed electricity markets, participants continuously monitor the cleared electricity prices and respond to them with the amount of energy they would like to p...Show MoreMetadata
Abstract:
In price directed electricity markets, participants continuously monitor the cleared electricity prices and respond to them with the amount of energy they would like to purchase. Thus, electricity purchase decisions are significantly facilitated by anticipating future consumption. In this paper, a data-driven method for anticipating the active power consumption in households is presented. In particular, Gaussian processes (GP) from the machine-learning realm are used for anticipation of electrical consumption at the level of individual households. Additionally, the performance of Gaussian processes equipped with various kernel functions is benchmarked against the approach of autoregressive moving average (ARMA) for anticipation of ten minute-ahead household consumption. The results indicate that GP outperforms ARMA in minute-ahead consumption anticipation, while there is not a dominant kernel that outperforms the rest within the GPR models.
Published in: 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)
Date of Conference: 06-08 July 2015
Date Added to IEEE Xplore: 21 January 2016
ISBN Information: