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Artificial Intelligence Techniques for Electrical Load Forecasting in Smart and Connected Communities

  • Victor Alagbe
  • Segun I. PopoolaEmail author
  • Aderemi A. Atayero
  • Bamidele Adebisi
  • Robert O. Abolade
  • Sanjay Misra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11623)

Abstract

Electricity consumption has been on a rapid increase worldwide and it is a very vital component of human life in this age. Hence, reliable supply of electricity from the utility operators is a necessity. However, the constraints that electricity supplied must be the same as electricity consumed puts the burden on the utility operators to make sure that demand is equal to supply at any point in time in smart and connected communities. Load forecasting techniques, therefore, aim to resolve these challenges for the operators by providing accurate forecasts of electrical load demand. This paper reviews current and mostly used short term forecasting techniques, drawing parallels be-tween them; and highlighting their advantages and disadvantages. This paper concludes by stating that there is no one-size-fits-all technique for load forecasting problems, as appropriate techniques depend on several factors such as data size and variability and environmental variables. Different optimization techniques can be used whether to reduce errors and its variations or to speed up computational time, hence resulting in an improved model. However, it is imperative to consider the tradeoffs between each model and its different variants in the context of smart and connected communities.

Keywords

Artificial Intelligence Load forecasting Smart city Neural network Support Vector Machine 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.IoT-Enabled Smart and Connected Communities (SmartCU) Research ClusterCovenant UniversityOtaNigeria
  2. 2.Department of EngineeringManchester Metropolitan UniversityManchesterUK
  3. 3.Department of Electronic and Electrical EngineeringLadoke Akintola University of TechnologyOgbomosoNigeria

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