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

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.