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Open AccessArticle

Short-Term Photovoltaic Power Prediction Using Nonlinear Spiking Neural P Systems

by Yunzhu Gao, Jun Wang, Lin Guo and Hong Peng

Sustainability 2024, 16(4), 1709; https://doi.org/10.3390/su16041709 - 19 Feb 2024

Cited by 15 | Viewed by 2616

To ensure high-quality electricity, improve the dependability of power systems, reduce carbon emissions, and promote the sustainable development of clean energy, short-term photovoltaic (PV) power prediction is crucial. However, PV power is highly stochastic and volatile, making accurate predictions of PV power very difficult. To address this challenging prediction problem, in this paper, a novel method to predict the short-term PV power using a nonlinear spiking neural P system-based ESN model has been proposed. First, we combine a nonlinear spiking neural P (NSNP) system with a neural-like computational model, enabling it to effectively capture the complex nonlinear trends in PV sequences. Furthermore, an NSNP system featuring a layer is designed. Input weights and NSNP reservoir weights are randomly initialized in the proposed model, while the output weights are trained by the Ridge Regression algorithm, which is motivated by the learning mechanism of echo state networks (ESNs), providing the model with an adaptability to complex nonlinear trends in PV sequences and granting it greater flexibility. Three case studies are conducted on real datasets from Alice Springs, Australia, comparing the proposed model with 11 baseline models. The outcomes of the experiments exhibit that the model performs well in tasks of PV power prediction. Full article

(This article belongs to the Topic Solar Forecasting and Smart Photovoltaic Systems)

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10 pages, 1835 KB

Open AccessCommunication

Short-Term Load Forecasting Based on Spiking Neural P Systems

by Lin Li, Lin Guo, Jun Wang and Hong Peng

Appl. Sci. 2023, 13(2), 792; https://doi.org/10.3390/app13020792 - 6 Jan 2023

Cited by 12 | Viewed by 2521

Abstract

Short-term load forecasting is a significant component of safe and stable operations and economical and reliable dispatching of power grids. Precise load forecasting can help to formulate reasonable and effective coordination plans and implementation strategies. Inspired by the spiking mechanism of neurons, a nonlinear spiking neural P (NSNP) system, a parallel computing model, was proposed. On the basis of SNP systems, this study exploits a fresh short-term load forecasting model, termed as the LF-NSNP model. The LF-NSNP model is essentially a recurrent-like model, which can effectively capture the correlation between the temporal features of the electric load sequence. In an effort to validate the effectiveness and superiority of the proposed LF-NSNP model in short-term load forecasting tasks, tests were conducted on datasets of different time and different variable types, and the predictive competence of various baseline models was compared. Full article

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17 pages, 1944 KB

Open AccessArticle

Spiking Neural Membrane Computing Models

by Xiyu Liu and Qianqian Ren

Processes 2021, 9(5), 733; https://doi.org/10.3390/pr9050733 - 21 Apr 2021

Cited by 4 | Viewed by 2861

Abstract

As third-generation neural network models, spiking neural P systems (SNP) have distributed parallel computing capabilities with good performance. In recent years, artificial neural networks have received widespread attention due to their powerful information processing capabilities, which is an effective combination of a class of biological neural networks and mathematical models. However, SNP systems have some shortcomings in numerical calculations. In order to improve the incompletion of current SNP systems in dealing with certain real data technology in this paper, we use neural network structure and data processing methods for reference. Combining them with membrane computing, spiking neural membrane computing models (SNMC models) are proposed. In SNMC models, the state of each neuron is a real number, and the neuron contains the input unit and the threshold unit. Additionally, there is a new style of rules for neurons with time delay. The way of consuming spikes is controlled by a nonlinear production function, and the produced spike is determined based on a comparison between the value calculated by the production function and the critical value. In addition, the Turing universality of the SNMC model as a number generator and acceptor is proved. Full article

(This article belongs to the Special Issue Modeling, Simulation and Design of Membrane Computing System)

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