Smart Grid Inspired Future Technologies. Second EAI International Conference, SmartGIFT 2017, London, UK, March 27–28, 2017, Proceedings

Research Article

Demand Profiling and Demand Forecast Using the Active-Aware-Based Ensemble Kalman Filter

Download
212 downloads
  • @INPROCEEDINGS{10.1007/978-3-319-61813-5_11,
        author={Eng Lau and Kok Chai and Yue Chen},
        title={Demand Profiling and Demand Forecast Using the Active-Aware-Based Ensemble Kalman Filter},
        proceedings={Smart Grid Inspired Future Technologies. Second EAI International Conference, SmartGIFT 2017, London, UK, March 27--28, 2017, Proceedings},
        proceedings_a={SMARTGIFT},
        year={2017},
        month={9},
        keywords={Demand profiling Demand forecast Ensemble Kalman Filter Data assimilation},
        doi={10.1007/978-3-319-61813-5_11}
    }
    
  • Eng Lau
    Kok Chai
    Yue Chen
    Year: 2017
    Demand Profiling and Demand Forecast Using the Active-Aware-Based Ensemble Kalman Filter
    SMARTGIFT
    Springer
    DOI: 10.1007/978-3-319-61813-5_11
Eng Lau1,*, Kok Chai1,*, Yue Chen1,*
  • 1: Queen Mary University of London
*Contact email: e.t.lau@qmul.ac.uk, michael.chai@qmul.ac.uk, yue.chen@qmul.ac.uk

Abstract

The concept of demand profiling is established in order to collect, analyse and develop the detailed knowledge of the consumption habits, either in domestic or non-domestic usage. In this paper the state representation of electrical signal is used as the profiling formula to model the diurnal (daily) and annual cycle demand trend of electricity consumption across the grid. The available demand dataset from the public domain is applied as the input for the profiling formula. The developed demand profile is further to be forecast and assimilated using the active-aware-based Ensemble Kalman Filter (EnKF). The resultant EnKF estimations may provide the assessment of nationwide demand within the energy network, thus consider the need for the present and future network reinforcement or upgrades. The ability of EnKF in forecasting the demand is presented, along with the limitations.