Urban mobility is a multidimensional characteristic of cities experienced as layers of interconnected infrastructures, places, people, and information. Therefore, the study of networks such as electricity and transportation systems should go beyond an individual network and merge with other networks. This paper proposes the bundled causality engine as a novel information theory-based approach to characterize the causal dependency between flows of electricity and transportation networks. To validate this hypothesis, electricity load forecasting is performed by combining transportation network data with the smart meter data for the City of Tallahassee, FL, USA. The results show a considerable improvement in the short-term load forecasting accuracy at the household level.


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    Title :

    Combined Electricity and Traffic Short-Term Load Forecasting Using Bundled Causality Engine




    Publication date :

    2019-09-01


    Size :

    3385469 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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