 An efficient robust optimized functional link broad learning system for solar irradiance predictionRanjeeta Bisoi, Deepak Ranjan Dash, P.K. Dash and Lokanath Tripathy Applied Energy, 2022, vol. 319, issue C, No S0306261922006341 Abstract: A new machine learning approach for forecasting short-termsolar irradiance is presented in this paper considering different weather conditions and time horizon in a microgrid environment. Using historical solar irradiance, a short-term forecasting model known as Robust optimized functional link broad learning system (FLBLS) is developed. Here the original input comprising delayed historical solar irradiance samples is initially expanded by exponential trigonometric functions and the expanded features are then used to create a large number of feature nodes using random weights and biases. Further the newly created features are fed as inputs to the enhancement nodes forming a broad learning system (BLS). This newly formed flat FLBLS facilitates incremental learning in fast remodelling of the network if needed for efficient performance without a retraining process unlike deep neural networks using several hidden layers and filters, etc. The proposed approach is designed on the basis of the standard random vector functional link network (RVFLN), however unlike the traditional RVFLN that applies inputs directly and set up the enhancement nodes, in our proposed approach, initially the inputs are expanded and map the expanded inputs to generate a set of mapped features. It reduces the lengthy process unlike deep neural networks that involves the back propagation algorithm for error minimization. The proposed FLBLS approach simplifies the learning process with the proper choice of feature nodes as it can be implemented to a flat network or a simpler network such as ELM that requires only the simple calculation of the output weight and obtain the final output where retraining is not required for adding extra feature nodes or enhancement nodes like deep neural architectures. Also, the minimum variance strategy with the training samples is useful in rejecting the effects of noise and outliers in the system and make the network robust which gets reflected in computing output weights using generalized least square. The minimum variance strategy further enhances the performance of the proposed system. To improve the prediction accuracy, the FLBLS parameters are optimized using an efficient metaheuristic chaotic firefly-sine cosine levy flight algorithm. Also, to reject the effects of noise and outliers and make the network robust a minimum variance strategy is used with the training samples that gets reflected in computing output weights using generalised least squares. The suggested method is experimented rigorously and analysed with different time frames designed for 30 min and 60 min ahead solar irradiance prediction. The obtained results from experiments confirm that the proposed FLBLS approach outperforms other randomized neural network forecasting methods as well many state-of-the-art methods using solar irradiance data of Hyderabad, India collected from National Renewable Energy Laboratory (NREL). This approach exhibits with the lowest errors in different case studies among all the compared methods. This FLBLS designed approach is an efficient approach for the purpose of solar irradiance prediction with highly nonlinear data. Keywords: Solar irradiance prediction; Random vector functional link network (RVFLN); Broad learning system (BLS); Robust exponential expanded random vector functional link net; Robust optimized functional link broad learning system (FLBLS); Exponential trigonometric functional expansion; Minimum variance; Deep learning; Firefly sine–cosine levy flight optimization algorithm (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:319:y:2022:i:c:s0306261922006341 Ordering information: This journal article can be ordered from DOI: 10.1016/j.apenergy.2022.119277 Access Statistics for this article Applied Energy is currently edited by J. Yan More articles in Applied Energy from Elsevier |
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