Abstract
In this paper, WESN (wavelet echo state network) with a novel ESN-based reconstruction stage is applied to both STLF (short-term load forecasting) and STTF (short-term temperature forecasting). Wavelet transform is used as the front stage for multi-resolution decomposition of load or temperature time series. ESNs function as forecasters for decomposed components. A modified shuffled frog leaping algorithm is used for optimizing ESNs. Both one-hour and 24-h ahead predictions are studied where the number of inputs are kept minimum. The performance of the proposed WESN-based load forecasters are investigated for three cases as the predicted temperature input is fed by actual temperatures, output of the WESN-based temperature forecasters and noisy temperatures. Effects of temperature errors on load forecasts are studied locally by sensitivity analysis. Hourly loads and temperatures of a North-American electric utility are used for this study. First, results of the proposed forecasters are compared with those of ESN-based forecasters that have previously shown high capability as stand-alone forecasters. Next, the WESN-based forecasters are compared with other models either previously tested on the data used here or to be rebuilt for testing on these data. Comparisons reveal significant improvements on accuracy of both STLF and STTF using the proposed forecasters.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.