| Naslov: | Methods and models for electric load forecasting : a comprehensive review |
|---|
| Avtorji: | ID Hammad, Mahmoud A. (Avtor)
ID Jereb, Borut (Avtor)
ID Rosi, Bojan (Avtor)
ID Dragan, Dejan (Avtor) |
|---|
| Datoteke: |  https://doi.org/10.2478/jlst-2020-0004
 RAZ_Hammad_Mahmoud_A._2020.pdf (1,23 MB)
MD5: C7E67326A55621F2F07655FCE37F95D5
|
|---|
| Jezik: | Angleški jezik |
|---|
| Vrsta gradiva: | Članek v reviji |
|---|
| Tipologija: | 1.01 - Izvirni znanstveni članek |
|---|
| Organizacija: | FL - Fakulteta za logistiko
|
|---|
| Opis: | Electric load forecasting (ELF) is a vital process in the planning of the electricity industry and plays a crucial role in electric capacity scheduling and power systems management and, therefore, it has attracted increasing academic interest. Hence, the accuracy of electric load forecasting has great importance for energy generating capacity scheduling and power system management. This paper presents a review of forecasting methods and models for electricity load. About 45 academic papers have been used for the comparison based on specified criteria such as time frame, inputs, outputs, the scale of the project, and value. The review reveals that despite the relative simplicity of all reviewed models, the regression analysis is still widely used and efficient for long-term forecasting. As for short-term predictions, machine learning or artificial intelligence-based models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Fuzzy logic are favored. |
|---|
| Ključne besede: | methods, models, electric load forecasting, modeling electricity loads, electricity industry, power management, logistics |
|---|
| Status publikacije: | Objavljeno |
|---|
| Verzija publikacije: | Objavljena publikacija |
|---|
| Datum objave: | 20.02.2020 |
|---|
| Leto izida: | 2020 |
|---|
| Št. strani: | str. 51-76 |
|---|
| Številčenje: | Letn. 11, št. 1 |
|---|
| PID: | 20.500.12556/DKUM-90103  |
|---|
| UDK: | 005.5 |
|---|
| COBISS.SI-ID: | 513089597 |
|---|
| DOI: | 10.2478/jlst-2020-0004 |
|---|
| ISSN pri članku: | 2232-4968 |
|---|
| Datum objave v DKUM: | 22.08.2024 |
|---|
| Število ogledov: | 220 |
|---|
| Število prenosov: | 27 |
|---|
| Metapodatki: |    |
|---|
| Področja: | Ostalo
|
|---|
|
:
|
Kopiraj citat |
|---|
| | | | Skupna ocena: | (0 glasov) |
|---|
| Vaša ocena: | Ocenjevanje je dovoljeno samo prijavljenim uporabnikom. |
|---|
| Objavi na: |  |
|---|
Postavite miškin kazalec na naslov za izpis povzetka. Klik na naslov izpiše
podrobnosti ali sproži prenos. |
