Journal
SUSTAINABLE CITIES AND SOCIETY
Volume 41, Issue -, Pages 205-219Publisher
ELSEVIER
DOI: 10.1016/j.scs.2018.05.009
Keywords
Building aggregated load forecast; Electric vehicles; Feature selection; Support vector machine
Ask authors/readers for more resources
In this paper, a new prediction model for aggregated loads of buildings has been propose. Due to high correlation of prediction performance with related horizons and aggregated more customers, a new strategy is developed to provide a forecasting model based on high accuracy. While, consumption shape of a single users normally has low structure to be exploited and vice versa. So, combining different users develops the relative prediction performance up to special point. Outside this point, no more enhancement in relative performance can be found. This model is useful in optimal power system operation and planning in micro-grids. In this paper, besides the aggregated loads of building, the electric vehicle (EV) impact on network has been considered. While, by considering the load growth prediction and impact of EV adaption on load curves this problem can be an important issue for the power grid operation and management. So, an accurate prediction model is presented in this work which is composed of new feature selection and enhanced support vector machine (ESVM) based forecast engine. Effectiveness of the proposed model is carried out to the part of Budapest city through comparing with other prediction models. Obtained results demonstrate the validity of proposed method.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available