A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management

A. Ghasemi, H. Shayeghi, M. Moradzadeh, M. Nooshyar

Research output: Contribution to journalArticle

73 Citations (Scopus)

Abstract

Smart grid is a platform that enables the participants of electricity market to adjust their bidding strategies based on Demand-Side Management (DSM) models. Responsiveness of the market participants can improve reliability of system operation as well as capital cost investments. In this regard, the accurate forecast of electricity price and demand in smart grids is an important challenge as their strong correlation makes a separate forecasting to be ineffective. Therefore, this paper proposes a novel hybrid algorithm for simultaneous forecast of price and demand that uses a set of effective tools in preprocessing part, forecast engine and tuned algorithm. To highlight our contributions, the proposed forecast algorithm classified into three main parts. The first part employs a new Flexible Wavelet Packet Transform (FWPT) to decompose a signal into multiple terms at different frequencies, and a new feature selection method that employs Conditional Mutual Information (CMI) and adjacent features in order to select valuable input data. The second part consists of a novel Multi-Input Multi-Output (MIMO) model based on Nonlinear Least Square Support Vector Machine (NLSSVM) and Autoregressive Integrated Moving Average (ARIMA) in order to model the linear and nonlinear correlation between price and load in two stages. The final part employs a modified version of Artificial Bee Colony (ABC) algorithm based on time-varying coefficients and stumble generation operator, called TV-SABC, in order to optimize NLSSVM parameters in a learning process. The proposed hybrid forecasting algorithm is evaluated on several real and well-known markets illustrating its high accuracy in simultaneous forecast of electricity price and demand. Moreover, the interactive effects of demand-side management programs on load factor (load curve) and price signal are investigated by numerical indices.

Original languageEnglish
Pages (from-to)40-59
Number of pages20
JournalApplied Energy
Volume177
Early online date21 May 2016
DOIs
Publication statusPublished - 1 Sep 2016

Fingerprint

demand-side management
electricity
Electricity
market
Support vector machines
bee
wavelet
Feature extraction
engine
transform
learning
forecast
price
smart grid
Demand side management
Mathematical transformations
Engines
cost
demand
Costs

Cite this

Ghasemi, A. ; Shayeghi, H. ; Moradzadeh, M. ; Nooshyar, M. / A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management. In: Applied Energy. 2016 ; Vol. 177. pp. 40-59.
@article{0c25b075d5fe486abdf0bf0a5218fccb,
title = "A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management",
abstract = "Smart grid is a platform that enables the participants of electricity market to adjust their bidding strategies based on Demand-Side Management (DSM) models. Responsiveness of the market participants can improve reliability of system operation as well as capital cost investments. In this regard, the accurate forecast of electricity price and demand in smart grids is an important challenge as their strong correlation makes a separate forecasting to be ineffective. Therefore, this paper proposes a novel hybrid algorithm for simultaneous forecast of price and demand that uses a set of effective tools in preprocessing part, forecast engine and tuned algorithm. To highlight our contributions, the proposed forecast algorithm classified into three main parts. The first part employs a new Flexible Wavelet Packet Transform (FWPT) to decompose a signal into multiple terms at different frequencies, and a new feature selection method that employs Conditional Mutual Information (CMI) and adjacent features in order to select valuable input data. The second part consists of a novel Multi-Input Multi-Output (MIMO) model based on Nonlinear Least Square Support Vector Machine (NLSSVM) and Autoregressive Integrated Moving Average (ARIMA) in order to model the linear and nonlinear correlation between price and load in two stages. The final part employs a modified version of Artificial Bee Colony (ABC) algorithm based on time-varying coefficients and stumble generation operator, called TV-SABC, in order to optimize NLSSVM parameters in a learning process. The proposed hybrid forecasting algorithm is evaluated on several real and well-known markets illustrating its high accuracy in simultaneous forecast of electricity price and demand. Moreover, the interactive effects of demand-side management programs on load factor (load curve) and price signal are investigated by numerical indices.",
keywords = "Demand-side management, Feature selection, Load and price forecasting, Smart grids, Wavelet transform",
author = "A. Ghasemi and H. Shayeghi and M. Moradzadeh and M. Nooshyar",
year = "2016",
month = "9",
day = "1",
doi = "10.1016/j.apenergy.2016.05.083",
language = "English",
volume = "177",
pages = "40--59",
journal = "Applied Energy",
issn = "0306-2619",
publisher = "Elsevier BV",

}

A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management. / Ghasemi, A.; Shayeghi, H.; Moradzadeh, M.; Nooshyar, M.

In: Applied Energy, Vol. 177, 01.09.2016, p. 40-59.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management

AU - Ghasemi, A.

AU - Shayeghi, H.

AU - Moradzadeh, M.

AU - Nooshyar, M.

PY - 2016/9/1

Y1 - 2016/9/1

N2 - Smart grid is a platform that enables the participants of electricity market to adjust their bidding strategies based on Demand-Side Management (DSM) models. Responsiveness of the market participants can improve reliability of system operation as well as capital cost investments. In this regard, the accurate forecast of electricity price and demand in smart grids is an important challenge as their strong correlation makes a separate forecasting to be ineffective. Therefore, this paper proposes a novel hybrid algorithm for simultaneous forecast of price and demand that uses a set of effective tools in preprocessing part, forecast engine and tuned algorithm. To highlight our contributions, the proposed forecast algorithm classified into three main parts. The first part employs a new Flexible Wavelet Packet Transform (FWPT) to decompose a signal into multiple terms at different frequencies, and a new feature selection method that employs Conditional Mutual Information (CMI) and adjacent features in order to select valuable input data. The second part consists of a novel Multi-Input Multi-Output (MIMO) model based on Nonlinear Least Square Support Vector Machine (NLSSVM) and Autoregressive Integrated Moving Average (ARIMA) in order to model the linear and nonlinear correlation between price and load in two stages. The final part employs a modified version of Artificial Bee Colony (ABC) algorithm based on time-varying coefficients and stumble generation operator, called TV-SABC, in order to optimize NLSSVM parameters in a learning process. The proposed hybrid forecasting algorithm is evaluated on several real and well-known markets illustrating its high accuracy in simultaneous forecast of electricity price and demand. Moreover, the interactive effects of demand-side management programs on load factor (load curve) and price signal are investigated by numerical indices.

AB - Smart grid is a platform that enables the participants of electricity market to adjust their bidding strategies based on Demand-Side Management (DSM) models. Responsiveness of the market participants can improve reliability of system operation as well as capital cost investments. In this regard, the accurate forecast of electricity price and demand in smart grids is an important challenge as their strong correlation makes a separate forecasting to be ineffective. Therefore, this paper proposes a novel hybrid algorithm for simultaneous forecast of price and demand that uses a set of effective tools in preprocessing part, forecast engine and tuned algorithm. To highlight our contributions, the proposed forecast algorithm classified into three main parts. The first part employs a new Flexible Wavelet Packet Transform (FWPT) to decompose a signal into multiple terms at different frequencies, and a new feature selection method that employs Conditional Mutual Information (CMI) and adjacent features in order to select valuable input data. The second part consists of a novel Multi-Input Multi-Output (MIMO) model based on Nonlinear Least Square Support Vector Machine (NLSSVM) and Autoregressive Integrated Moving Average (ARIMA) in order to model the linear and nonlinear correlation between price and load in two stages. The final part employs a modified version of Artificial Bee Colony (ABC) algorithm based on time-varying coefficients and stumble generation operator, called TV-SABC, in order to optimize NLSSVM parameters in a learning process. The proposed hybrid forecasting algorithm is evaluated on several real and well-known markets illustrating its high accuracy in simultaneous forecast of electricity price and demand. Moreover, the interactive effects of demand-side management programs on load factor (load curve) and price signal are investigated by numerical indices.

KW - Demand-side management

KW - Feature selection

KW - Load and price forecasting

KW - Smart grids

KW - Wavelet transform

UR - http://www.scopus.com/inward/record.url?scp=84969228171&partnerID=8YFLogxK

U2 - 10.1016/j.apenergy.2016.05.083

DO - 10.1016/j.apenergy.2016.05.083

M3 - Article

VL - 177

SP - 40

EP - 59

JO - Applied Energy

JF - Applied Energy

SN - 0306-2619

ER -