Photovoltaic Power Prediction Under Insufficient Historical Data Based on Dendrite Network and Coupled Information Analysis

Lu, Tianhao; Wang, Chunsheng; Cao, Yuan; Chen, Hong

doi:10.2139/ssrn.4184484

Cited by 1 publication

(1 citation statement)

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“…Literature [11] Convolutional dendrite net detects myocardial infarction based on ECG signal measured by flexible sensor Literature [25] Photovoltaic Power Prediction Under Insufficient Historical Data Based on Dendrite Network and Coupled Information Analysis Literature [26] The limitation of this paper comes from the lack of engineering data. In this paper, DD with AC is fundamentally verified by many experiments, which is a stronger way to prove it than using special data.…”

Section: Applications Literaturementioning

confidence: 99%

Dendrite Net with Acceleration Module for Faster Nonlinear Mapping and System Identification

et al. 2022

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Nonlinear mapping is an essential and common demand in online systems, such as sensor systems and mobile phones. Accelerating nonlinear mapping will directly speed up online systems. Previously the authors of this paper proposed a Dendrite Net (DD) with enormously lower time complexity than the existing nonlinear mapping algorithms; however, there still are redundant calculations in DD. This paper presents a DD with an acceleration module (AC) to accelerate nonlinear mapping further. We conduct three experiments to verify whether DD with AC has lower time complexity while retaining DD’s nonlinear mapping properties and system identification properties: The first experiment is the precision and identification of unary nonlinear mapping, reflecting the calculation performance using DD with AC for basic functions in online systems. The second experiment is the mapping precision and identification of the multi-input nonlinear system, reflecting the performance for designing online systems via DD with AC. Finally, this paper compares the time complexity of DD and DD with AC and analyzes the theoretical reasons through repeated experiments. Results: DD with AC retains DD’s excellent mapping and identification properties and has lower time complexity. Significance: DD with AC can be used for most engineering systems, such as sensor systems, and will speed up computation in these online systems.

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Section: Applications Literaturementioning

confidence: 99%