A Multi-step ahead photovoltaic power forecasting model based on TimeGAN, Soft DTW-based K-medoids clustering, and a CNN-GRU hybrid neural network
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In this paper , a multi-step ahead PV power forecasting (PPF) model, which combines time-series generative adversarial networks (TimeGAN), soft dynamic time warping (DTW)-based K-medoids clustering algorithms, and a hybrid neural network model computed by a convolutional neural network (CNN) and gated recurrent units (GRU), was proposed.About:
This article is published in Energy Reports.The article was published on 2022-11-01 and is currently open access. It has received 8 citations till now. The article focuses on the topics: Cluster analysis & Dynamic time warping.read more
Citations
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Research on Ultra-Short-Term Load Forecasting Based on Real-Time Electricity Price and Window-Based XGBoost Model
TL;DR: By simulating the load data of Singapore’s electricity market, it is proved that the proposed ultra-short-term load forecasting method has fewer errors than other deep learning algorithms, and the introduction of the real-time electricity price helps to improve the prediction accuracy of the model.
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Multitasking recurrent neural network for photovoltaic power generation prediction
Nameer Al Khafaf,Ammar Kamoona,Samaneh Sadat Sajjadi,Ali Moradi Amani,Mahdi Jalili,Xinghuo Yu,P. McTaggart +6 more
TL;DR: In this paper , a multitasking RNN (MT-RNN) framework was proposed for predicting PV power generation over different categories of customers (e.g., residential, agricultural, industrial, and commercial) using a single model.
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An approach for day-ahead interval forecasting of photovoltaic power: A novel DCGAN and LSTM based quantile regression modeling method
TL;DR: In this paper , a day-ahead interval forecasting method of PV power based on multi-correlation parameter scenarios generation is proposed, where the historical PV power data is divided into a limited set of scenarios representing different output and fluctuation characteristics through the K-means clustering algorithm.
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Deep Belief Network with Swarm Spider Optimization Method for Renewable Energy Power Forecasting
TL;DR: In this paper , a novel metaheuristic optimization algorithm, called swarm spider optimization (SSO), was utilized to optimize the parameters of the DBN so as to improve its performance.
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Vision Transformer-Based Photovoltaic Prediction Model
TL;DR: In this article , a visual transformer model for photovoltaic (PV) prediction is proposed, in which the target PV sensor information and the surrounding PV sensor auxiliary information are used as input data.
References
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TL;DR: The analysis of recent advances in genetic algorithms is discussed and the well-known algorithms and their implementation are presented with their pros and cons with the aim of facilitating new researchers.
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Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
Justin Salamon,Juan Pablo Bello +1 more
TL;DR: It is shown that the improved performance stems from the combination of a deep, high-capacity model and an augmented training set: this combination outperforms both the proposed CNN without augmentation and a “shallow” dictionary learning model with augmentation.
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A global averaging method for dynamic time warping, with applications to clustering
TL;DR: A global technique for averaging a set of sequences is developed, which avoids using iterative pairwise averaging and is thus insensitive to ordering effects, and a new strategy to reduce the length of the resulting average sequence is described.
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Forecasting of photovoltaic power generation and model optimization: A review
Utpal Kumar Das,Kok Soon Tey,Mohammadmehdi Seyedmahmoudian,Saad Mekhilef,Moh Yamani Idna Idris,Willem Van Deventer,Ben Horan,Alex Stojcevski +7 more
TL;DR: In this paper, a comprehensive and systematic review of the direct forecasting of PV power generation is presented, where the importance of the correlation of the input-output data and the preprocessing of model input data are discussed.
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Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines
TL;DR: In this paper, a one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM.