The complexity of the electric grid is increasing as a variety of different renewable energy sources are being added to it. This requires the formulation of more advanced and accurate shortterm electric load forecasting (STLF) techniques for smooth operation of the grid. The work presented in this thesis proposes an online multifunctional linear regression model for STLF. The research is done in an incremental manner with the development of an offline functional linear regression model in the first instance. The proposed technique uses functional representation of daily historical load values to predict the future daily load function. Functional linear regression models are created, and their functional regression parameters estimated. The performance of the model is evaluated by using real world load data. The results obtained show a better performance than existing functional linear regression techniques for STLF. As compared to nonfunctional methods, the functional technique has the advantage that it gives a functional output with more detailed insight about the load. The offline functional linear regression model was then extended to include multiple functional inputs to give a more general regression model. The performance was evaluated on the same realworld data and it outperformed the previous model that used only one functional input. A novel online realtime functional regression model was then proposed which only requires the current input functions to update the functional regression parameters. The updated regression parameter function is found by using a functional recursive least squares algorithm. The performance of the online model was evaluated by using the same realworld data. The results show that it has an added advantage of being much faster computationally and not requiring any storage of historical load data. This model was then extended to include multiple functional inputs to give a more general online regression model. Such a model has an added advantage of considering a variety of factors to give a better prediction result.
Date of Award  31 Dec 2020 

Original language  English 

Awarding Institution   The University of Manchester


Supervisor  Xiaojun Zeng (Supervisor) & Alessandra Parisio (Supervisor) 

 Electric Load Forecasting
 Functional Data Analysis
 Functional Regression
 Shortterm Load Forecasting
A FUNCTIONAL REGRESSION APPROACH TO SHORTTERM ELECTRIC LOAD FORECASTING
Kiani, H. (Author). 31 Dec 2020
Student thesis: Phd