The ongoing deployment of residential smart meters in numerous jurisdictions has led to an influx of electricity consumption data. This information presents a valuable opportunity to suppliers for ...better understanding their customer base and designing more effective tariff structures. In the past, various clustering methods have been proposed for meaningful customer partitioning. This paper presents a novel finite mixture modeling framework based on C-vine copulas (CVMM) for carrying out consumer categorization. The superiority of the proposed framework lies in the great flexibility of pair copulas toward identifying multidimensional dependency structures present in load profiling data. CVMM is compared to other classical methods by using real demand measurements recorded across 2613 households in a London smart-metering trial. The superior performance of the proposed approach is demonstrated by analyzing four validity indicators. In addition, a decision tree classification module for partitioning new consumers is developed and the improved predictive performance of CVMM compared to existing methods is highlighted. Further case studies are carried out based on different loading conditions and different sets of large numbers of households to demonstrate the advantages and to test the scalability of the proposed method.
Decarbonization of electricity systems drives significant and continued investments in distributed energy sources to support the cost-effective transition to low-carbon energy systems. However, the ...rapid integration of distributed photovoltaic (PV) generation presents great challenges in obtaining reliable and secure grid operations because of its limited visibility and intermittent nature. Under this reality, net load forecasting is facing unprecedented difficulty in answering the following question: How can we accurately predict the net load while capturing the massive uncertainties arising from distributed PV generation and load, especially in the context of high PV penetration? This paper proposes a novel probabilistic day-ahead net load forecasting method to capture both epistemic uncertainty and aleatoric uncertainty using Bayesian deep learning, which is a new field that combines Bayesian probability theory and deep learning. The proposed methodological framework employs clustering in subprofiles and considers residential rooftop PV outputs as input features to enhance the performance of aggregated net load forecasting. Numerical experiments have been carried out based on fine-grained smart meter data from the Australian grid with separately recorded measurements of rooftop PV generation and loads. The results demonstrate the superior performance of the proposed scheme compared with a series of state-of-the-art methods and indicate the importance and effectiveness of subprofile clustering and high PV visibility.
Bi-level optimization and reinforcement learning (RL) constitute the state-of-the-art frameworks for modeling strategic bidding decisions in deregulated electricity markets. However, the former ...neglects the market participants' physical non-convex operating characteristics, while conventional RL methods require discretization of state and/or action spaces and thus suffer from the curse of dimensionality. This paper proposes a novel deep reinforcement learning (DRL) based methodology, combining a deep deterministic policy gradient (DDPG) method with a prioritized experience replay (PER) strategy. This approach sets up the problem in multi-dimensional continuous state and action spaces, enabling market participants to receive accurate feedback regarding the impact of their bidding decisions on the market clearing outcome, and devise more profitable bidding decisions by exploiting the entire action domain, also accounting for the effect of non-convex operating characteristics. Case studies demonstrate that the proposed methodology achieves a significantly higher profit than the alternative state-of-the-art methods, and exhibits a more favourable computational performance than benchmark RL methods due to the employment of the PER strategy.
•Propose a novel pinball loss guided long short-term memory network.•Design probabilistic forecasting model for individual consumers.•Conduct comprehensive comparisons with the-state-of-the-art ...methods.•Conduct case studies on open dataset and large number of consumers.
The installation of smart meters enables the collection of massive fine-grained electricity consumption data and makes individual consumer level load forecasting possible. Compared to aggregated loads, load forecasting for individual consumers is prone to non-stationary and stochastic features. In this paper, a probabilistic load forecasting method for individual consumers is proposed to handle the variability and uncertainty of future load profiles. Specifically, a deep neural network, long short-term memory (LSTM), is used to model both the long-term and short-term dependencies within the load profiles. Pinball loss, instead of the mean square error (MSE), is used to guide the training of the parameters. In this way, traditional LSTM-based point forecasting is extended to probabilistic forecasting in the form of quantiles. Numerical experiments are conducted on an open dataset from Ireland. Forecasting for both residential and commercial consumers is tested. Results show that the proposed method has superior performance over traditional methods.
Adequate capacity planning of substations and feeders primarily depends on an accurate estimation of the future peak electricity demand. Traditional coincident peak demand estimation is carried out ...based on the empirical metric, after diversity maximum demand, indicating individual peak consumption levels and demand diversification across multiple residents. With the privilege of smart meters in smart cities, this paper proposes a data-driven probabilistic peak demand estimation framework using fine-grained smart meter data and sociodemographic data of the consumers, which drive fundamental electricity consumptions across different categories. In particular, four main stages are integrated in the proposed approach: load modeling and sampling via the proposed variable truncated R-vine copulas method, correlation-based customer grouping, probabilistic normalized maximum diversified demand estimation, and probabilistic peak demand estimation for new customers. Numerical experiments have been conducted on real demand measurements across 2639 households in London, collected from Low Carbon London project's smart-metering trial. The mean absolute percentage error and the pinball loss function are used to quantitatively demonstrate the superiority of the proposed approach in terms of the point estimate value and the probabilistic result, respectively.
This study defines financially distressed enterprises based on stock delisting risk warnings and uses the annual data of A-share listed companies on the Shanghai and Shenzhen stock exchanges from ...2008 to 2021 to examine the impact and mechanism of financial distress on the capital expenditures of non-distressed enterprises in the same city. The results indicate that if financially distressed enterprises exist within a city, the capital expenditure of non-distressed enterprises within the same city will subsequently decrease. The conclusions hold after multiple robustness and endogeneity tests. Mechanism tests show that financially distressed enterprises reduce the operating performance and cash flow of non-distressed enterprises in the same city through business performance contagion, thereby reducing their intrinsic motivation for capital expenditure. However, financial distress enhances creditors’ credit risk perceptions of non-distressed enterprises in the same city through signal transmission effects, prompting creditors to tighten credit contracts or directly intervene in corporate capital expenditure decisions, thus suppressing corporate capital expenditure. Heterogeneity tests indicate that the smaller the asset size of non-distressed enterprises, the larger the scale of financially distressed enterprises relative to non-distressed enterprises in the same city, or the more severe the agency problem of non-distressed enterprises or degree of financial distress, the more significant the negative externality of financial distress on the capital expenditures of local enterprises. The economic consequences test shows that the reduction effect of financial distress on the capital expenditure of non-distressed enterprises in the same city ultimately improves their capital expenditure efficiency.
Stereopsis is the ability of human beings to get the 3D perception on real scenarios. The conventional stereopsis measurement is based on subjective judgment for stereograms, leading to be easily ...affected by personal consciousness. To alleviate the issue, in this paper, the EEG signals evoked by dynamic random dot stereograms (DRDS) are collected for stereogram recognition, which can help the ophthalmologists diagnose strabismus patients even without real-time communication. To classify the collected Electroencephalography (EEG) signals, a novel multi-scale temporal self-attention and dynamical graph convolution hybrid network (MTS-DGCHN) is proposed, including multi-scale temporal self-attention module, dynamical graph convolution module and classification module. Firstly, the multi-scale temporal self-attention module is employed to learn time continuity information, where the temporal self-attention block is designed to highlight the global importance of each time segments in one EEG trial, and the multi-scale convolution block is developed to further extract advanced temporal features in multiple receptive fields. Meanwhile, the dynamical graph convolution module is utilized to capture spatial functional relationships between different EEG electrodes, in which the adjacency matrix of each GCN layer is adaptively tuned to explore the optimal intrinsic relationship. Finally, the temporal and spatial features are fed into the classification module to obtain prediction results. Extensive experiments are conducted on collected datasets i.e., SRDA and SRDB, and the results demonstrate the proposed MTS-DGCHN achieves outstanding classification performance compared with the other methods. The datasets are available at https://github.com/YANGeeg/TJU-SRD-datasets and the code is at https://github.com/YANGeeg/MTS-DGCHN .
While wetlands are usually used as a natural approach to remove biodegradable pollutants in surface water, their purification efficiencies coupled with their aesthetic features are of less concern. ...The water quality, plant landscape, acoustic environment and odour indicators were investigated in the surface water inlet and outlet of the Fujin National Wetland Park (FNWP), restored from farmlands in Northeast China. Major concentrations of pollutants in the inlet and the outlet subjected to surface flow wetland treatment were monitored, and the removal efficiencies were calculated based on 54 water samples (6 sites×3 seasons×3 replicates). The results showed that the total nitrogen (TN) and organic carbon in surface water decreased significantly after the wetland treatment, while the total phosphorus (TP) did not decrease significantly. The removal efficiencies for TN and BOD5 changed seasonally and reached 69.08% and 60.44%, respectively. An ecological aesthetic index (EAI) was developed based on the trophic state index coupled with plant landscape, acoustic and odour indicators, and the calculated EAI showed that the outlet delivered a more aesthetically harmonious appearance than the inlet in spring and autumn, but not in summer. Based on the current aquatic macrophyte species and documented purification efficiencies in FNWP, we recommend an improved ecological aesthetic management approach that utilizes and arranges diverse native plants from the surrounding wetlands (e.g. Scirpus validus) in addition to local Nelumbo nucifera, Nymphaea tetragona and Myriophyllum spicatum, and conserves the indicative and endangered species (Aldrovanda vesiculosa), from the visual appeal of the waterscape.
Berberine (BBR), as a natural isoquinoline alkaloid, has demonstrated various pharmacological activities, and is widely applied in the treatment of diseases. The quantitative analysis of BBR is ...important for pharmacological studies and clinical applications. In this work, utilizing the specific interaction between BBR and triplex DNA, a sensitive and selective fluorescent detecting method was established with DNA-templated silver nanoclusters (DNA-AgNCs). After binding with the triplex structure in the template of DNA-AgNCs, BBR quenched the fluorescence of DNA-AgNCs and formed BBR-triplex complex with yellow–green fluorescence. The ratiometric fluorescence signal showed a linear relationship with BBR concentration in a range from 10 nM to 1000 nM, with a detection limit of 10 nM. Our method exhibited excellent sensitivity and selectivity, and was further applied in BBR detection in real samples.
Genitourinary syndrome of menopause (GSM) is a disease caused by a physiological decline in estrogen levels, and it can negatively affect a woman's overall health and quality of life in terms of ...sexual function. Real-time optical biopsy images can now be obtained with optical coherence tomography (OCT) systems. In this study, we introduce vision transformer (ViT) to the field of medical OCT images for the first time and propose a deep learning-based approach for GSM lesion screening. Specifically, we first build a GSM dataset to train and evaluate the experimental model performance. The study aims to propose a method that combines null convolution with a deep convolutional adversarial generative network classifier to generate the samples needed for training to alleviate the hindrance of such problems, in response to certain practical problems, such as category imbalance that occur during data collection. Next, the experiments present ViT PLUS (ViT-P) for the vaginal OCT image classification task used, which effectively improves the shortcomings of ViT in extracting Patch Embedding using a multibranch convolutional neural network combined with a channel attention mechanism. The clinical images acquired by the OCT device are then automatically classified on the basis of the OCT device to reduce the medical workload of gynecologists. Experimental results show that the ViT-P model outperforms the CNN model and ViT for case screening in the GSM and UCSD datasets, and the accuracy can reach 99.9% and 99.69%, respectively.
PDF
