Name
Title | ||
|---|---|---|
A Survey on Short-Term Electricity Price Prediction Models for Smart Grid Applications |
Abstract | ||
|---|---|---|
In this paper we present a survey of recent trends on short-term electricity-price prediction models. We classify the proposed price prediction methods based on the forecasting horizon into short-medium- and long-term approaches. We provide the key features of the medium- and long-solutions, while we emphasize on short-term prediction models, by providing their classification into statistical, computational intelligent and hybrid methods. We also highlight the key characteristics of the available prediction methods, while the strengths and weaknesses of these solutions are also discussed and analyzed. These important aspects should be considered by researchers that target on the derivation of more efficient and accurate electricity-price prediction models, especially for smart grid applications. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-18802-7_9 | Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering |
Keywords | Field | DocType |
Electricity pricing,Prediction method,Price forecasting,Computational intelligence,Smart grid | Industrial engineering,Smart grid,Computational intelligence,Computer science,Predictive modelling,Electricity price,Strengths and weaknesses,Distributed computing,Electricity pricing,Price prediction | Conference |
Volume | ISSN | Citations |
146 | 1867-8211 | 1 |
PageRank | References | Authors |
0.43 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. S. Vardakas | 1 | 98 | 17.23 |
| Ioannis Zenginis | 2 | 1 | 0.43 |