Use of Available Daylight to Improve Short-Term Load Forecasting Accuracy

Miguel López, Sergio Valero, Carlos Sans and Carolina Senabre
Additional contact information
Miguel López: Department of Mechanic Engineering and Energy, Universidad Miguel Hernández, 03202 Elche, Spain
Sergio Valero: Department of Mechanic Engineering and Energy, Universidad Miguel Hernández, 03202 Elche, Spain
Carlos Sans: Department of Mechanic Engineering and Energy, Universidad Miguel Hernández, 03202 Elche, Spain
Carolina Senabre: Department of Mechanic Engineering and Energy, Universidad Miguel Hernández, 03202 Elche, Spain

Energies, 2020, vol. 14, issue 1, 1-14

Abstract: This paper introduces a new methodology to include daylight information in short-term load forecasting (STLF) models. The relation between daylight and power consumption is obvious due to the use of electricity in lighting in general. Nevertheless, very few STLF systems include this variable as an input. In addition, an analysis of one of the current STLF models at the Spanish Transmission System Operator (TSO), shows two humps in its error profile, occurring at sunrise and sunset times. The new methodology includes properly treated daylight information in STLF models in order to reduce the forecasting error during sunrise and sunset, especially when daylight savings time (DST) one-hour time shifts occur. This paper describes the raw information and the linearization method needed. The forecasting model used as the benchmark is currently used at the TSO’s headquarters and it uses both autoregressive (AR) and neural network (NN) components. The method has been designed with data from the Spanish electric system from 2011 to 2017 and tested over 2018 data. The results include a justification to use the proposed linearization over other techniques as well as a thorough analysis of the forecast results yielding an error reduction in sunset hours from 1.56% to 1.38% for the AR model and from 1.37% to 1.30% for the combined forecast. In addition, during the weeks in which DST shifts are implemented, sunset error drops from 2.53% to 2.09%.

Keywords: daylight; load forecasting; Power Demand (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/1/95/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/1/95/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2020:i:1:p:95-:d:468767

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().