Abstract With the introduction of national policies, photovoltaic (PV) power forecasting requirements for PV power plants are becoming increasingly stringent. It is particularly critical that PV power predictions are accurate while new energy is being consumed. It is also important to consider the satellite imagery of the location of the PV power plant and the meteorological information of the plant itself. The authors aim to explore the impact of these two elements on PV power prediction to better support PV power prediction. Therefore, this paper explores the cloud information elements of the satellite images from a multi‐view perspective and performs feature extraction and processing of the meteorological information to learn the impact of cloud cover on PV power prediction. Meanwhile, this paper introduces the mutual information mechanism for the influence of meteorological factors on PV power generation. It constructs the mutual information matrix and adopts the graph neural network for representation learning. A time‐series prediction model for short‐term PV power prediction is constructed and more accurate prediction results are obtained. The experimental results demonstrate that the proposed method is effective, has generalisation ability, and improved performance compared with the traditional model. The proposed method can also provide a novel approach and solution for short‐term PV power prediction.

Abstract Clean energy is a major trend. The importance of photovoltaic power generation is also growing. Photovoltaic power generation is mainly affected by the weather. It is full of uncertainties. Previous work has relied chiefly on historical photovoltaics data for time series forecasts. However, unforeseen weather conditions can sometimes skew. Consequently, a spatial‐temporal‐meteorological‐long short‐term memory prediction model (STM‐LSTM) is proposed to compensate for the shortage of photovoltaic prediction models for uncertainties. This model can simultaneously process satellite image data, historical meteorological data, and historical power generation data. In this way, historical patterns and meteorological change information are extracted to improve the accuracy of photovoltaic prediction. STM‐LSTM processes raw satellite data to obtain cloud image data. It can extract cloud motion information using the dense optical flow method. First, the cloud images are processed to extract cloud position information. By adaptive attentive learning of images in different bands, a better representation for subsequent tasks can be obtained. Second, it is important to process historical meteorological data to learn meteorological change patterns. Last but not least, the historical photovoltaic power generation sequences are combined to obtain the final photovoltaic prediction results. After a series of experimental validation, the performance of the proposed STM‐LSTM model has a good improvement compared with the baseline model.

Any-to-any voice conversion can be performed among arbitrary speakers, even with a single reference utterance. Many related studies have demonstrated that it can be effectively implemented by speech representation disentanglement. However, most existing solutions fuse the speaker representations into the content features globally without considering their distribution difference. Additionally, in the any-to-any scenario, there is no effective method ensuring the consistency of linguistic content without text transcription or additional information extracted from additional modules (e.g., automatic speech recognition). Hence, to alleviate the above problems, this paper proposes SACS-VC, a novel any-to-any voice conversion method that combines two principal modules: Speaker Adaptation and Content Supervision. Specifically, we rearrange the timbre representations according to the content distribution using a temporal attention mechanism to obtain finer-grained speaker timbre information for each content feature. Meanwhile, we associate the converted outputs and source utterances directly to supervise the consistency of the semantic content in an unsupervised manner. This is achieved using contrastive learning based on the corresponding and non-corresponding locations of content features. It should be noted that SACS-VC can be implemented using a non-parallel speech corpus without any pertaining. The experimental results demonstrate that the proposed method outperforms current state-of-the-art any-to-any voice conversion systems in objective and subjective evaluation settings.

Emotional Voice Conversion (EVC) technology aims to transfer emotional state in speech while keeping the linguistic information and speaker identity unchanged. Prior studies on EVC have been limited to perform the conversion for a specific speaker or a predefined set of multiple speakers seen in the training stage. When encountering arbitrary speakers that may be unseen during training (outside the set of speakers used in training), existing EVC methods have limited conversion capabilities. However, converting the emotion of arbitrary speakers, even those unseen during the training procedure, in one model is much more challenging and much more attractive in real-world scenarios. To address this problem, in this study, we propose SIEVC, a novel speaker-independent emotional voice conversion framework for arbitrary speakers via disentangled representation learning. The proposed method employs the autoencoder framework to disentangle the emotion information and emotion-independent information of each input speech into separated representation spaces. To achieve better disentanglement, we incorporate mutual information minimization into the training process. In addition, adversarial training is applied to enhance the quality of the generated audio signals. Finally, speaker-independent EVC for arbitrary speakers could be achieved by only replacing the emotion representations of source speech with the target ones. The experimental results demonstrate that the proposed EVC model outperforms the baseline models in terms of objective and subjective evaluation for both seen and unseen speakers.

Arbitrary voice conversion, also referred to as zero-shot voice conversion, has recently attracted increased attention in the literature.Although disentangling the linguistic and style representations for acoustic features is an effective way to achieve zero-shot voice conversion, the problem of how to convert to a natural speaker style is challenging because of the intrinsic variabilities of speech and the difficulties of completely decoupling them.For this reason, in this paper, we propose a Two-Pathway Style Embedding Voice Conversion framework (TPSE-VC) for realistic and natural speech conversion.The novel feature of this method is to simultaneously embed sentence-level and phoneme-level style information.A novel attention mechanism is proposed to implement the implicit alignment for timbre style and phoneme content, further embedding a phoneme-level style representation.In addition, we consider embedding the complete set of time steps of audio style into a fixed-length vector to obtain the sentence-level style representation.Moreover, TPSE-VC does not require any pre-trained models, and is only trained with non-parallel speech data.Experimental results demonstrate that the proposed TPSE-VC outperforms the state-of-theart results on zero-shot voice conversion.

Synthesizing a frontal view face from a single nonfrontal image, i.e. face frontalization, is a task of practical importance in a wide range of facial image analysis applications. However, to train the frontalization model in a supervised manner, most existing face frontalization methods rely on the availability of nonfrontal-frontal face pairs (typically from the Multi-PIE dataset) captured in a constrained environment. Such approaches, in return, limit the generalizability of their application to unconstrained scenarios. Unfortunately, although a large amount of in-the-wild face datasets are available, they cannot easily be utilized for face frontalization training since the nonfrontal and frontal facial images are not paired. To train a frontalization network which generalizes well to both constrained and unconstrained environments, we propose a semi-supervised learning framework which effectively uses both (labeled) indoor and (unlabeled) outdoor faces. Specifically, to achieve this goal, this article presents a Cycle-Consistent Face Frontalization Generative Adversarial Network (CCFF-GAN) which consists of both (1) the supervised and (2) the unsupervised components. For (1), we use the indoor paired (labeled) data to learn a roughly accurate frontalization network which may not generalize well to outdoor (in-the-wild) scenarios. For (2), to cope with the generalization issue, the unsupervised part uses the unpaired (unlabeled) images under the perceptual cycle consistency constraint in the semantic feature space to generalize the network from controlled (indoor) to uncontrolled (outdoor) environment. Extensive experiments demonstrate the effectiveness of the proposed method in comparison with the state-of-the-art face frontalization methods, especially under the in-the-wild scenarios.