Deep learning based solar radiation micro forecast by fusion of infrared cloud images and radiation data
TL;DR: In this paper, a multi-modal fusion network is developed for studying solar irradiance micro forecasts by using both infrared images and past solar irradiances data, where both spatial and temporal information is extracted parallelly and fused using a fully connected neural network.
About: This article is published in Applied Energy.The article was published on 2021-07-15. It has received 37 citations till now. The article focuses on the topics: Solar irradiance & Mean absolute percentage error.
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TL;DR: The obtained results show that the proposed method exhibits accurate and robust forecasting performance and outperforms conventional regression models.
54 citations
TL;DR: In this paper , a deep solar forecasting approach is proposed based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), continuous wavelet transform (CWT), feature extraction networks, RReliefF feature selection, and extreme learning machine (ELM).
44 citations
TL;DR: In this article , an end-to-end short-term forecasting model is proposed to take satellite images as inputs, and it can learn the cloud motion characteristics from stacked optical flow maps.
Abstract: Developing solar power generation technology is an efficient approach to relieving the global environmental crisis. However, solar energy is an energy source with strong uncertainty, which restricts large-scale photovoltaic (PV) applications until accurate solar energy predictions can be achieved. PV power forecasting methods have been widely researched based on existing predictions of satellite-derived solar irradiance, whereas modeling cloud motion directly from satellite images is still a tough task. In this study, an end-to-end short-term forecasting model is proposed to take satellite images as inputs, and it can learn the cloud motion characteristics from stacked optical flow maps. In order to reduce the huge size of measurements, static regions of interest (ROIs) are scoped based on historical cloud velocities. With its well-designed deep learning architecture, the proposed model can output multi-step-ahead prediction results sequentially by shifting receptive attention to dynamic ROIs. According to comparisons with related studies, the proposed model outperforms persistence and derived methods, and enhances its learning capability relative to conventional learning models via the novel architecture. The model can be applied to PV plants or arrays in different areas, suitable for forecast horizons within three hours.
19 citations
TL;DR: In this article, the progress in intra-hour solar forecasting methodologies are comprehensively reviewed and concisely summarized and suggestions to accelerate the development of future intrahour forecasting methods are provided.
15 citations
TL;DR: In this paper, a hybrid model-driven method and a data-driven control method using machine learning algorithms were proposed to realize the extraction of the data hiding mode, where the Koopman operation was used to increase the dimension of data to be linearized.
12 citations
References
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TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Abstract: Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
72,897 citations
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01 Jan 1995TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
40,147 citations
Book•
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TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Abstract: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
38,208 citations
Posted Content•
TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.
Abstract: We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.
23,486 citations
01 Jan 2014
TL;DR: In this paper, the encoder and decoder of the RNN Encoder-Decoder model are jointly trained to maximize the conditional probability of a target sequence given a source sequence.
Abstract: In this paper, we propose a novel neural network model called RNN Encoder‐ Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder‐Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.
19,998 citations