Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation

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ID: 266322
2017
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Abstract
This paper discusses short-term electricity-load forecasting using an extreme learning machine (ELM) with automatic knowledge representation from a given input-output data set. For this purpose, we use a Takagi-Sugeno-Kang (TSK)-based ELM to develop a systematic approach to generating if-then rules, while the conventional ELM operates without knowledge information. The TSK-ELM design includes a two-phase development. First, we generate an initial random-partition matrix and estimate cluster centers for random clustering. The obtained cluster centers are used to determine the premise parameters of fuzzy if-then rules. Next, the linear weights of the TSK fuzzy type are estimated using the least squares estimate (LSE) method. These linear weights are used as the consequent parameters in the TSK-ELM design. The experiments were performed on short-term electricity-load data for forecasting. The electricity-load data were used to forecast hourly day-ahead loads given temperature forecasts; holiday information; and historical loads from the New England ISO. In order to quantify the performance of the forecaster, we use metrics and statistical characteristics such as root mean squared error (RMSE) as well as mean absolute error (MAE), mean absolute percent error (MAPE), and R-squared, respectively. The experimental results revealed that the proposed method showed good performance when compared with a conventional ELM with four activation functions such sigmoid, sine, radial basis function, and rectified linear unit (ReLU). It possessed superior prediction performance and knowledge information and a small number of rules.
Reference Key
kwak2017energiesshort-term Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chan-Uk Yeom,Keun-Chang Kwak;Chan-Uk Yeom;Keun-Chang Kwak;
Journal energies
Year 2017
DOI
10.3390/en10101613
URL
https://sciprofiles.com/publication/view/4393ebe1be2e8a8ffa4dedc5f69790f2
Keywords
knowledge representation extreme learning machine hybrid learning short-term electricity-load forecasting tsk fuzzy type knowledge representation extreme learning machine hybrid learning short-term electricity-load forecasting tsk fuzzy type

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