Forecasting of real-time electricity load has been an important research topic over many years. Electricity load is driven by many factors, including economic conditions and weather. Furthermore, the demand for electricity varies with time, with different hours of the day and different days of the week having an effect on the load. This paper proposes a hybrid load-forecasting method that combines classical time series formulations with cubic splines to model electricity load. It is shown that this approach produces a model capable of making short-term forecasts with reasonable accuracy. In contrast to forecasting models that utilize a multitude of regressor variables observed at multiple time points within a day, only the hourly temperature is used in the proposed model and predictive power gains are achieved through the modeling of the 24-hour load profiles across weekends and weekdays while also taking into consideration seasonal variations of such profiles. Long-term trends are accounted for by using population and economic variables. The proposed approach can be used as a stand-alone predictive platform or be used as a scaffolding to build a more complex model involving additional inputs. The data cover the period from 1 January 1993 through 31 December 2013 from the Atlantic City Electric zone.


Mathematics and Statistics

Research Center/Lab(s)

Center for High Performance Computing Research

Keywords and Phrases

Cubic Splines; Forecasting; Real-Time Electricity Load; Seasonal Patterns; Time Series

International Standard Serial Number (ISSN)

1996-1073; 1996-1073

Document Type

Article - Journal

Document Version

Final Version

File Type





© 2019 The Authors, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

01 Nov 2019