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.
Highlights
The electric power system is often considered the most complex system devised by humans.It consists of dynamic components, including transformers, generators, transmission and distribution lines, nonlinear and linear loads, protective devices, etc
We observed that the extreme learning machine (ELM) with the sine, sigmoid, and radial basis function showed similar performance except for rectified linear unit (ReLU) activation function
We proposed a new design method based on an ELM with automatic knowledge representation We proposed a new design method based on an ELM with automatic knowledge representation from the numerical data sets for short-term electricity-load forecasting
Summary
The electric power system is often considered the most complex system devised by humans. Dong [3] proposed a forecasting method model using data decomposition approach for short-term electricity load forecasting. Li [16] proposed a novel wavelet-based ensemble method for short-term load forecasting with hybrid neural networks and feature selection. Li [20] proposed an ensemble method for short-term load forecasting based on a wavelet transform, an ELM, and partial least squares regression. These ELM methods have demonstrated their prediction and classification superiority, it is difficult to obtain meaningful information. We propose a new ELM predictor with knowledge representation, e.g., if- rules, for short-term electricity-load forecasting.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
