NDLI: Deep learning architecture for direct probability density prediction of small-scale solar generation

Content Provider	IET Digital Library
Author	Afrasiabi, Mousa Mohammadi, Mohammad Rastegar, Mohammad Afrasiabi, Shahabodin
Abstract	With the increasing penetration of photovoltaic (PV) systems, the problems posed by the inherent intermittency of small-scale PVs are becoming more severe. To address this issue, it is critical to involve the uncertainty of PV generation in the look-ahead periods in a comprehensive framework. To this end, a direct deep learning architecture for probabilistic forecasting of solar generation is proposed in this paper. An end-to-end deep learning architecture as a novel mixture density network (MDN) is designed based on the combination of a convolutional neural network and a gated recurrent unit. Furthermore, a new loss function and training process based on adversarial training is proposed to enhance the accuracy in direct contracting of the probability density function. Then, several deep and shallow networks are implemented, and the results are compared with the proposed architecture. The effectiveness of the proposed MDN in providing complete statistical information is verified through comparison with Monte Carlo dropout, non-parametric kernel density estimation, and the proposed MDN without adversarial training.
Starting Page	2017
Ending Page	2025
Page Count	9
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 11, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2019.1289
Journal	IET Generation, Transmission & Distribution
Publisher Date	2020-01-13
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adversarial Training Convolutional Neural Nets Convolutional Neural Network Deep Network Direct Contracting Direct Deep Learning Architecture Direct Probability Density Prediction End-to-end Deep Learning Architecture Inherent Intermittency Knowledge Engineering Technique Learning in AI Look-ahead Periods Loss Function MDN Neural Computing Technique Nonparametric Kernel Density Estimation Novel Mixture Density Network Photovoltaic Power System Photovoltaic System Power Engineering Computing Probabilistic Forecasting Probability Probability Density Function PV Generation Shallow Network Small-scale PVs Small-scale Solar Generation Solar Power Stations Statistics Training Process
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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