MODELING OF MULTI INPUT MULTI OUTPUT BASED LSSVM FOR ELECTRICITY PRICE AND LOAD FORECASTING IN SMART GRID WITH CONSIDERING DEMAND SIDE MANAGEMENT

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SHAYEGHI HOSSEIN; GHASEMI ALI

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Journal: COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING) Year:2016 | Volume:6 | Issue:4 Page(s): 87-106

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Information Journal Paper

Title

MODELING OF MULTI INPUT MULTI OUTPUT BASED LSSVM FOR ELECTRICITY PRICE AND LOAD FORECASTING IN SMART GRID WITH CONSIDERING DEMAND SIDE MANAGEMENT

Author(s)

SHAYEGHI HOSSEIN | GHASEMI ALI | Issue Writer Certificate

Keywords

WAVELET TRANSFORMSQ2

LSSVMQ2

FUZZY MUTUAL INFORMATION (FMI)Q2

PRICE AND LOAD FORECASTINGQ1

DEMAND SIDE MANAGEMENTQ1

Abstract

In smart grids, customers will be enabled to change their strategies by electricity prices. In fact, in smart grid, we obvious a great correlation between price and load signals which show the market participants will have complex model in their decisions to maximize their profit. Many pervious-studies forecasted load or price independently. But they were not suitable for smart grid environment. To overcome this shortage, we present Multi-Input Multi-Output based Least Squares Support Vector Machine (MIMO-LSSVM) forecasted engine which can consider the correlation between price and load signals in simultaneous model. In other words, this paper presents a new hybrid algorithm to forecast day-ahead price and load in the electricity market. It consists of four stages known as a Discrete Wavelet Transform (DWT) to make valuable subsets, fuzzy mutual information (FMI) to select best input candidate and LSSVM-MIMO model. Finally, the LSSVM-MIMO parameters are optimized by a novel Improved Artificial Bee Colony (IABC) algorithm. Some forecasting indexes based on error factor are considered to shows the forecasting accuracy. Simulation results are examined on New England and New South Wales (NSW) Zone in Australia’s electricity markets. The numerical simulation results show that the proposed hybrid algorithm has good potential for forecasting simultaneous loap/price problems.

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APA: Copy

SHAYEGHI, HOSSEIN, & GHASEMI, ALI. (2016). MODELING OF MULTI INPUT MULTI OUTPUT BASED LSSVM FOR ELECTRICITY PRICE AND LOAD FORECASTING IN SMART GRID WITH CONSIDERING DEMAND SIDE MANAGEMENT. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), 6(4), 87-106. SID. https://sid.ir/paper/202955/en

Vancouver: Copy

SHAYEGHI HOSSEIN, GHASEMI ALI. MODELING OF MULTI INPUT MULTI OUTPUT BASED LSSVM FOR ELECTRICITY PRICE AND LOAD FORECASTING IN SMART GRID WITH CONSIDERING DEMAND SIDE MANAGEMENT. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)[Internet]. 2016;6(4):87-106. Available from: https://sid.ir/paper/202955/en

IEEE: Copy

HOSSEIN SHAYEGHI, and ALI GHASEMI, “MODELING OF MULTI INPUT MULTI OUTPUT BASED LSSVM FOR ELECTRICITY PRICE AND LOAD FORECASTING IN SMART GRID WITH CONSIDERING DEMAND SIDE MANAGEMENT,” COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), vol. 6, no. 4, pp. 87–106, 2016, [Online]. Available: https://sid.ir/paper/202955/en

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