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Controllable distributed energy resources (DERs) in active distribution grids (ADGs) provide operational flexibility to system operators, thereby, offering the means to address various challenges. Existing local controllers for these resources are communication-free, robust, and cheap, but with suboptimal performance compared to centralized approaches that heavily rely on monitoring and communication. Data-driven local controls can bridge the gap by providing customized local controllers designed from historical data, offline optimization, and machine learning methods. These local controllers emulate the optimal behavior under expected operating conditions, without the use of communication. However, they exhibit high implementation overhead with the need of individual programming of DER controllers, especially when there are many DERs or when new units are installed at a later stage. In this article, we propose a clustering method to decrease the implementation overhead by reducing the individual DER controls into a smaller set while still achieving high performance. We show the performance of the method on a three-phase, unbalanced, low-voltage, distribution network. [ABSTRACT FROM AUTHOR]

The existing neural network model in urban land-subsidence prediction is over-reliant on historical subsidence data. It cannot accurately capture or predict the fluctuation in the sequence deformation, while the improper selection of training samples directly affects its final prediction accuracy for large-scale urban land subsidence. In response to the shortcomings of previous urban land-subsidence predictions, a subsidence prediction method based on a neural network algorithm was constructed in this study, from a multi-factorial perspective. Furthermore, the scientific selection of a large range of training samples was controlled using a K-shape clustering algorithm in order to produce this high-precision urban land subsidence prediction method. Specifically, the main urban area of Kunming city was taken as the research object, LiCSBAS technology was adopted to obtain the information on the land-subsidence deformation in the main urban area of Kunming city from 2018–2021, and the relationship between the land subsidence and its influencing factors was revealed through a grey correlation analysis. Hydrogeology, geological structure, fault, groundwater, high-speed railways, and high-rise buildings were selected as the influencing factors. Reliable subsidence training samples were obtained by using the time-series clustering K-shape algorithm. Particle swarm optimization–back propagation (PSO-BP) was constructed from a multi-factorial perspective. Additionally, after the neural network algorithm was employed to predict the urban land subsidence, the fluctuation in the urban land-subsidence sequence deformation was predicted with the LSTM neural network from a multi-factorial perspective. Finally, the large-scale urban land-subsidence prediction was performed. The results demonstrate that the maximum subsidence rate in the main urban area of Kunming reached −30.591 mm⋅a−1 between 2018 and 2021. Moreover, there were four main significant subsidence areas in the whole region, with uneven distribution characteristics along Dianchi: within the range of 200–600 m from large commercial areas and high-rise buildings, within the range of 400–1200 m from the under-construction subway, and within the annual average. The land subsidence tended to occur within the range of 109–117 mm of annual average rainfall. Furthermore, the development of faults destroys the stability of the soil structure and further aggravates the land subsidence. Hydrogeology, geological structure, and groundwater also influence the land subsidence in the main urban area of Kunming. The reliability of the training sample selection can be improved by clustering the subsidence data with the K-shape algorithm, and the constructed multi-factorial PSO-BP method can effectively predict the subsidence rate with a mean squared error (MSE) of 4.820 mm. The prediction accuracy was slightly improved compared to the non-clustered prediction. We used the constructed multi-factorial long short-term memory (LSTM) model to predict the next ten periods of any time-series subsidence data in the three types of cluster data (Cluster 1, Cluster 2, and Cluster 3). The root mean square errors (RMSE) were 0.445, 1.475, and 1.468 mm; the absolute error ranges were 0.007–1.030, 0–3.001, and 0.401–3.679 mm; the errors (mean absolute error, MAE) were 0.319, 1.214, and 1.167 mm, respectively. Their prediction accuracy was significantly improved, and the predictions met the measurement specifications. Overall, the prediction method proposed from the multi-factorial perspective improves large-scale, high-accuracy urban land-subsidence prediction.

Due to the material variations of lithium-ion cells and fluctuations in their manufacturing precision, differences exist in electrochemical characteristics of cells, which inevitably lead to a reduction in the available capacity and premature failure of a battery pack with multiple cells configured in series, parallel, and series–parallel. Screening cells that have similar electrochemical characteristics to overcome the inconsistency among cells in a battery pack is a challenging problem. This paper proposes an approach for lithium -ion cell screening using convolutional neural networks (CNNs) based on two-step time-series clustering (TTSC) and hybrid resampling for imbalanced data, which takes into account the dynamic characteristics of lithium-ion cells, thus ensuring that the screened cells have similar electrochemical characteristics. In this approach, we propose the TTSC to label the raw samples and propose the hybrid resampling method to solve the sample imbalance issue, thereby obtaining labeled and balanced datasets and establishing the CNN model for online cell screening. Finally, industrial applications verify the effectiveness of the proposed approach and the inconsistency rate of the screened cells drops by 91.08%.

One of the golden rules in instructional design methods is to optimize the use of working memory capacity and avoid cognitive overload. The study of cognitive load has historically relied on one's introspection. However, it is difficult to capture changes in cognitive load levels during learning sensitively. This paper suggests an approach to investigating dynamic changes in cognitive load by using a pupillometry. With the method, this study explores the effects of learners' prior knowledge and task complexity on cognitive load. An experiment was conducted on two groups of students (N = 19) with distinct levels of prior knowledge. In the experimental session, participants watched a video lecture on a mathematics proposition, while being eye‐tracked. The lecture consists of sections, which can be either a high task complexity or a low task complexity based on elements they have. Pupil dilations acquired in each section were used to explore the time course of cognitive load. To formulate cognitive load patterns, a time‐series clustering was used. The research conducted a chi‐square analysis to test differences in cognitive load patterns by prior knowledge and task complexity. Results show that pupil dilation patterns can be applied to monitor changes in cognitive load during learning. What is already known about this topic? Cognitive load is a dynamic construct so that it can be changed during learning.Pupil dilation is a valid indicator of showing changes in cognitive load during learning. What this paper adds? The study proposes a novel method to capture dynamic changes in cognitive load in video‐based learning by using a time‐series clustering.The proposed approach detected the changes in cognitive load as a function of learner's level of prior knowledge and task complexity in learning.The proposed approach found seven different types of cognitive load patterns during learning. Implications for practice and/or policy: The use of temporal approach can be a technique to capture dynamics of cognitive load learners are experiencing during learning. [ABSTRACT FROM AUTHOR]

Adynamic texture is a spatio-temporal generative model for video, which represents video sequences as observations from a linear dynamical system. This work studies the mixture of dynamic textures, a statistical model for an ensemble of video sequences that is sampled from a finite collection of visual processes, each of which is a dynamic texture. An expectation-maximization (EM) algorithm is derived for learning the parameters of the model, and the model is related to previous works in linear systems, machine learning, time-series clustering, control theory, and computer vision. Through experimentation, it is shown that the mixture of dynamic textures is a suitable representation for both the appearance and dynamics of a variety of visual processes that have traditionally been challenging for computer vision (for example, fire, steam, water, vehicle and pedestrian traffic, and so forth). When compared with state-of-the-art methods in motion segmentation, including both temporal texture methods and traditional representations (for example, optical flow or other localized motion representations), the mixture of dynamic textures achieves superior performance in the problems of clustering and segmenting video of such processes.

The COVID-19 pandemic has impacted ridesourcing services dramatically, but empirical research on disparities in the resilience and recovery of ridesourcing has been scarce. To address this literature gap, we used ridesourcing trip data in Chicago to create two time series: one for Census tract-level ridesourcing usage (including pickups and dropoffs) and the other for linkages between origin and destination (OD) pairs. We performed time-series clustering analyses that integrated manifold learning and Gaussian Mixture Modeling to optimize the number of clusters for high-dimensional time-series data. The tract-level usage can be grouped into three clusters, and the OD-pair linkages can be grouped into six clusters. We examined the spatial patterns of the tract-level usage clusters and the OD-pair linkage clusters. Furthermore, we estimated a multinomial logit regression model to examine the relationships between clusters and land use, built environment, and sociodemographic factors. Our results suggested that the share of residential land use had a positive association with high resilience and fast recovery of ridesourcing usage. Limited transportation accessibility and a lack of alternative transportation modes were also associated with high resilience and fast recovery of ridesourcing usage. Trips that linked dense employment centers were less likely to be made during the pandemic. Census tracts with a greater share of minorities or a higher poverty rate tended to generate more ridesourcing trips during the pandemic. [ABSTRACT FROM AUTHOR]

• A new application of time-series clustering to deal with low data quality and uncommon patterns. • A validated MLP model for heat load prediction of two different building types. • A comparison between MLP with online inputs and ANN approach with historical data. • An evaluation of impact of two clustering algorithms on prediction performance. Current district heating networks are undergoing a sustainable transition towards the 4th and 5th generation of district heating networks, characterized by the integration of different types of renewable energy sources (RES) and low operational temperatures, i.e., 55 °C or lower. Due to the lower temperature difference between supply and return, it is necessary to develop novel methods to understand the loads accurately and provide operation scenarios to anticipate demand peaks and increase flexibility in the energy network, both for long- and short-term horizons. In this study, a hybrid machine-learning (ML) method is developed, combining a clustering pre-processing step with a multi-input artificial neural network (ANN) model to predict heat loads in buildings cluster-wise. Specifically, the impact of time-series data clustering, as a pre-processing step, on the performance of ML models was investigated. It was found that data clustering contributes effectively to the reduction of data training costs by limiting the training processes to representative clusters only instead of all datasets. Additionally, low-quality data, including outliers and large measurement gaps, are excluded from the training to enhance the overall prediction performance of the models. [ABSTRACT FROM AUTHOR]

The Arctic Ocean is one of the regions where anthropogenic environmental change is progressing most rapidly and drastically. The impact of rising temperatures and decreasing sea ice on Arctic marine microbial communities is yet not well understood. Microbes form the basis of food webs in the Arctic Ocean, providing energy for larger organisms. Previous studies have shown that Atlantic taxa associated with low light are robust to more polar conditions. We compared to which extent sea ice melt influences light-associated phytoplankton dynamics and biodiversity over two years at two mooring locations in the Fram Strait. One mooring is deployed in pure Atlantic water, and the second in the intermittently ice-covered Marginal Ice Zone. Time-series analysis of amplicon sequence variants abundance over a 2-year period, allowed us to identify communities of co-occurring taxa that exhibit similar patterns throughout the annual cycle. We then examined how alterations in environmental conditions affect the prevalence of species. During high abundance periods of diatoms, polar phytoplankton populations dominated, while temperate taxa were weakly represented. Furthermore, we found that polar pelagic and ice-associated taxa, such as Fragilariopsis cylindrus and Melosira arctica , were more common in Atlantic conditions, while temperate taxa, such as Odontella aurita and Proboscia alata , were less abundant under polar conditions. This suggests that sea ice melt may act as a barrier to the northward expansion of temperate phytoplankton, preventing their dominance in regions still strongly influenced by polar conditions. Our findings highlight the complex interactions between sea ice melt, phytoplankton dynamics, and biodiversity in the Arctic., Competing Interests: The authors declare no competing interests., (© The Author(s) 2024. Published by Oxford University Press on behalf of the International Society for Microbial Ecology.)

Temporal trends and spatial patterns of six warm season (May–September) hot temperature indicators (WSHTIs) were developed and explored for Saudi Arabia. The indicators focus on the frequency and intensity of hot days and nights, and heat waves. Systematic upward trends in maximum (Tmax) and minimum (Tmin) temperatures were found at most of the stations, suggesting ongoing change in the climatology of the upper-tail of the frequency distribution. Taking into the account the observed effects of climate change on the country's climate, hot temperature events were defined using a monthly and decadal, time-sensitive approach. Indicators of event frequency are count data; thus, different Poisson models were used for temporal analysis. Further, a novel method of time-series clustering was introduced to recognize spatiotemporal patterns of WSHTIs. Different patterns were observed over time and space not only across stations but also among WSHTIs. Generally, warming trends were detected in the upper limits for both Tmax and Tmin across the warm season months with a few surprising exceptions, mostly for Tmax. Results suggest that the impact of climate change on hot weather events was more pronounced at night at most of the stations in Saudi Arabia. The overall results suggest that not only local and regional factors, such as elevation, latitude, and distance from a large body of water, but also large-scale factors such as atmospheric circulation patterns are likely responsible for the observed temporal and spatial patterns. [ABSTRACT FROM AUTHOR]

With the development of films, games and animation industry, analysis and reuse of human motion capture data become more and more important. Human motion segmentation, which divides a long motion sequence into different types of fragments, is a key part of mocap-based techniques. However, most of the segmentation methods only take into account low-level physical information (motion characteristics) or high-level data information (statistical characteristics) of motion data. They cannot use the data information fully. In this paper, we propose an unsupervised framework using both low-level physical information and high-level data information of human motion data to solve the human segmentation problem. First, we introduce the algorithm of CFSFDP and optimize it to carry out initial segmentation and obtain a good result quickly. Second, we use the ACA method to perform optimized segmentation for improving the result of segmentation. The experiments demonstrate that our framework has an excellent performance. [ABSTRACT FROM AUTHOR]

Abstract In this paper, time-series clustering is discussed. At first ℓ 1 trend filtering method is used to produce an optimal segmentation of time series. Next optimized fuzzy information granulation is completed for each segment to form a linear fuzzy information granule, which includes both average and trend information. Once the optimal segmentation and granulation have been completed, the original time series is transformed into a granular time series. To finalize time-series clustering, a distance measure for granular time series is established, and a linear fuzzy information granule-based dynamic time warping (LFIG_DTW) algorithm is developed for calculating the distance of two equal-length or unequal-length granular time series. Furthermore, the distance realized by the LFIG_DTW algorithm can detect not only the increasing or decreasing trends, but also the changing periods and rates of changes. After calculating all the distances between any two granular time series, a LFIG_DTW distance-based hierarchical clustering method is designed for time-series clustering. Experiment results involving several real datasets show the effectiveness of the proposed method. Highlights • Propose an optimal method for time series granulation based on ℓ 1 trend filtering and LFIG. • Give a new distance of LFIGs that generalizes the version of equal-size case. • Propose a distance measure method for granular time series composed of unequal-size LFIGs. • Propose a clustering method based on the proposed distance measure. [ABSTRACT FROM AUTHOR]

As COVID-19 vaccines were administered in early 2021, they helped to mitigate the spread of the COVID-19 virus and signaled an important shift in the pandemic. To better understand how ongoing COVID-19 vaccinations were related to human mobility across the U.S., we identified different mobility-vaccination profiles between January and May 2021 by county in the U.S., using K-means multivariate time-series clustering. The impacts of demographic, socioeconomic, and COVID-19-related variables on different profiles were examined. Results showed 5 different clusters of mobility-vaccination profiles were found for the U.S. One cluster represented counties in larger population centers and metropolitan areas (e.g., Los Angeles and New York) that had estimated 25% higher mobility and 75% higher vaccination rates than rural counties in the Mountain and South U.S. Census regions (e.g., counties in Arkansas and Mississippi), where people were mobile despite not getting vaccinated. Higher education and household income were found to impact counties' mobility-vaccination profiles. Examination of trip purposes for selected counties returned higher trips to retail/recreation and workplaces for rural counties with relatively lower mobility-vaccination profiles. The results can serve as input for regional and local health officials regarding population responses to a pandemic relevant to economic recovery and future disease prevention. • Statewide mobility differences were found as COVID-19 vaccinations were administered in 2021. • Analysis showed five clusters with different travel and vaccine uptake behaviors among counties. • Cluster with lower mobility and vaccination rates is mostly in Mountain and South states. • Education and economic factors contributed to cluster differences in mobility. [ABSTRACT FROM AUTHOR]

PM 2.5 pollutions is closely associated with vertical boundary-layer (BL) meteorological conditions. However, the vertical profiles of major meteorological elements are rarely classified and used to quantify unfavourable meteorological conditions and surface PM 2.5 concentration changes. This study uses time-series clustering to classify radiosonde observations in the winter of 2012– 2021 in Beijing. Temperature stratification classification was found to quantify the extent of unfavourable meteorological conditions well. The worst meteorological conditions often corresponded to an extremely stable boundary layer that became relatively warmer in the upper part and cooled in the near-ground region. Temperature inversions move downwards, forcing vertically distributed PM 2.5 to accumulate towards the surface. This upper BL warming in the Beijing area originated from southerly warm air flows, while the lower BL cooling partly resulted from the cooling effects of accumulated aerosols. From 2012 to 2016 to 2017– 2021, the most significant difference appeared in the proportions of severely unfavourable vertical conditions, which dropped by 47% from 3.2% in 2017 to 1.7% in 2021. This proportional decline in severely unfavourable vertical conditions was estimated to contribute to ~23% of the PM 2.5 concentration decline (16.8 μg m−3) from 2017 to 2021. The interaction between aerosol pollution and the vertical meteorological structure was an essential contributing factor to near-surface pollution. We found that the degree of modification of unfavourable meteorological conditions and the corresponding increase in the surface PM 2.5 concentrations could be estimated by the proportional change in the stable BL vertical structure. These changes could also be inferred from an autoregressive integrated moving average (ARIMA) model based on long-term balloon observations since 1991. The proportion of heavily unfavourable conditions was projected to increase from close to 0% in winter 2021 to 2.5% in winter 2022. Therefore, we suggest that the PM 2.5 reduction target for the following winter should consider the effect of unfavourable meteorological conditions. • Cluster vertical unfavourable meteorological structures using machine learning. • Obtain relationships between various vertical meteorological patterns and PM 2.5 mass. • Estimate the relative contributions of meteorology and emissions on PM 2.5 changes. • Predict the trend of PM 2.5 mass by vertical meteorological profiles. • Severe meteorological vertical conditions can impact near-surface PM 2.5. [ABSTRACT FROM AUTHOR]

In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time in a data-driven manner, aiming to provide fundamental knowledge that benefits policy-making for better mitigation strategies of future pandemics. Taking Metro Atlanta as a study case, we perform a trend-driven analysis by conducting Kmeans time-series clustering using fine-grained home dwell time records from SafeGraph. Furthermore, we apply ANOVA (Analysis of Variance) coupled with post-hoc Tukey’s test to assess the statistical difference in sixteen recoded demographic/socioeconomic variables (from ACS 2014–2018 estimates) among the identified time-series clusters. We find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order, which potentially leads to disparate exposures to the risk from the COVID-19. The results further suggest that socially disadvantaged groups are less likely to follow the order to stay at home, pointing out the extensive gaps in the effectiveness of social distancing measures that exist between socially disadvantaged groups and others. Our study reveals that the long-standing inequity issue in the U.S. stands in the way of the effective implementation of social distancing measures.

A well-planned irrigation management strategy is crucial for successful wine grape production and is highly dependent on accurate assessments of water stress. Precision irrigation practices may benefit from the quantification of within-field spatial variability and temporal patterns of evapotranspiration (ET). A spatiotemporal modeling framework is proposed to delineate the vineyard into homogeneous areas (i.e., management zones) according to their ET patterns. The dataset for this study relied on ET retrievals from multiple satellite platforms, generating estimates at high spatial (30 m) and temporal (daily) resolutions for a Vitis vinifera Pinot noir vineyard in the Central Valley of California during the growing seasons of 2015-2018. Time-series decomposition was used to deconstruct the time series of each pixel into three components: long-term trend, seasonality, and remainder, which indicates daily fluctuations. For each time-series component, a time-series clustering (TSC) algorithm was applied to partition the time series of all pixels into homogeneous groups and generate TSC maps. The TSC maps were compared for spatial similarities using the V-measure statistic. A random forest (RF) classification algorithm was used for each TSC map against six environmental variables (elevation, slope, northness, lithology, topographic wetness index, and soil type) to check for spatial association between ET-TSC maps and the local characteristics in the vineyard. Finally, the TSC maps were used as independent variables against yield (ton ha-1) using analysis of variance (ANOVA) to assess whether the TSC maps explained yield variability. The trend and seasonality TSC maps had a moderate spatial association (V = 0.49), while the remainder showed dissimilar spatial patterns to seasonality and trend. The RF model showed high error matrix-based prediction accuracy levels ranging between 86% and 90%. For the trend and seasonality models, the most important predictor was soil type, followed by elevation, while the remainder TSC was strongly linked with northness spatial variability. The yield levels corresponding to the two clusters in all TSC were significantly different. These findings enabled spatial quantification of ET time series at different temporal scales that may benefit within-season decision-making regarding the amounts, timing, intervals, and location of irrigation. The proposed framework may be applicable to other cases in both agricultural systems and environmental modeling.

This study forecasts electricity demand in a smart grid environment. We present a prediction method that uses a combination of forecasting values based on time-series clustering. The clustering of normalized periodogram-based distances and autocorrelation-based distances are proposed as the time-series clustering methods. Trigonometrical transformation, Box–Cox transformation, autoregressive moving average (ARMA) errors, trend and seasonal components (TBATS), double seasonal Holt–Winters (DSHW), fractional autoregressive integrated moving average (FARIMA), ARIMA with regression (Reg-ARIMA), and neural network nonlinear autoregressive (NN-AR) are used for demand forecasting based on clustering. The results show that the time-series clustering method performs better than the method using the total amount of electricity demand in terms of the mean absolute percentage error (MAPE).

A wireless sensor network (WSN) deploys hundreds or thousands of nodes that may introduce large-scale data over time. Dealing with such an amount of collected data is a real challenge for energy-constraint sensor nodes. Therefore, numerous research works have been carried out to design efficient data clustering techniques in WSNs to eliminate the amount of redundant data before transmitting them to the sink while preserving their fundamental properties. This paper develops a new error-aware data clustering (EDC) technique at the cluster-heads (CHs) for in-network data reduction. The proposed EDC consists of three adaptive modules that allow users to choose the module that suits their requirements and the quality of the data. The histogram-based data clustering (HDC) module groups temporal correlated data into clusters and eliminates correlated data from each cluster. Recursive outlier detection and smoothing (RODS) with HDC module provides error-aware data clustering, which detects random outliers using temporal correlation of data to maintain data reduction errors within a predefined threshold. Verification of RODS (V-RODS) with HDC module detects not only random outliers but also frequent outliers simultaneously based on both the temporal and spatial correlations of the data. The simulation results show that the proposed EDC is computationally cheap, able to reduce a significant amount of redundant data with minimum error, and provides efficient error-aware data clustering solutions for remote monitoring environmental applications.

The COVID-19 pandemic is a public health crisis that also fuels the pervasive social inequity in the United States. Existing studies have extensively analyzed the inequity issues on mobility across different demographic groups during the lockdown phase. However, it is unclear whether the mobility inequity is perennial and will continue into the mobility recovery phase. This study utilizes ride-hailing data from Jan 1st, 2019, to Mar 31st, 2022, in Chicago to analyze the impact of various factors, such as demographic, land use, and transit connectivity, on mobility inequity in the different recovery phases. Instead of commonly used statistical methods, this study leverages advanced time-series clustering and an interpretable machine learning algorithm. The result demonstrates that inequity still exists in the mobility recovery phase of the COVID-19 pandemic, and the degree of mobility inequity in different recovery phases is varied. Furthermore, mobility inequity is more likely to exist in the census tract with more families without children, lower health insurance coverage, inflexible workstyle, more African Americans, higher poverty rate, fewer commercial land use, and higher Gini index. This study aims to further the understanding of the social inequity issue during the mobility recovery phase of the COVID-19 pandemic and help governments propose proper policies to tackle the unequal impact of the pandemic., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. All rights reserved.)

Abstract: Because of the significant industrial demands towards quality and safety of system, reliability prediction with historical failures data has generated broad interest. Particularly, for system‐oriented failures time‐series data, although the hybridization strategy has been exploited to separately predict the feature components extracted from the original data and achieved noteworthy performance, a convictive method for effectively extracting these feature components has not been explored. In this paper, we introduce weighted shape‐based time‐series clustering to improve the hybrid modeling and prediction, in which a novel distance metric named as w_SBD (ie, weighted shape‐based distance) is devised by fully considering the shapes of time series and the characteristics of failures prediction. Moreover, we further develop a flexible framework to extract and validate the feature components (named as FF_EVFC). In the framework, besides w_SBD, 3 kinds of validations for the extracted feature components are also involved. To demonstrate the robustness of w_SBD and FF_EVFC, we perform extensive experimental evaluations with different clustering and prediction methods. The results show a competitive performance of w_SBD against other common distance metrics and verify the effectiveness of FF_EVFC on the improvement of failures prediction. [ABSTRACT FROM AUTHOR]

Understanding the seasonality of, and contributors to, surface urban heat island (SUHI) will pave the way for developing more generalized urban cooling strategies. The direction and shape of SUHI seasonal hysteresis have been thought to be background climate dependent. However, there remains a relative paucity of knowledge about the patterns and driving mechanisms of such hysteretic phenomena across diverse climates. Here we show that the seasonal hysteretic cycles of SUHI in the daytime and nighttime across Chinese cities between 2003 and 2020 can be identified as eight and six primary patterns, characterized by diverse and complex behaviors including clockwise, anti-clockwise, twisted, concave, and convex directions/loops. The urban-rural differences in evapotranspiration (ΔET) and surface albedo (ΔABD) have been recognized as the primary contributors to SUHI in the daytime and nighttime and their seasonal hysteresis, respectively, as measured by structural equation modeling (SEM) and time lead/lag analysis. These biophysical mechanisms for diverse seasonal hysteretic cycles of SUHI in China re-emphasized climate context dependencies in SUHI terms. Furthermore, the observed divergence of SUHI hysteresis typologies within similar background climates highlighted the additional drivers of complex urban systems because SUHI is a combined result of rural background and urban characteristics. We conclude that the cost-effective UHI mitigation strategies creating synergies or avoiding trade-offs among seasons should be climate context-specific and tailored to local urbanicity. • More diverse SUHI seasonal hysteresis across Chinese cities has been identified. • Re -emphasized climate context dependencies of SUHI patterns and dynamics. • Urban and rural surface elements together shaped the SUHI hysteresis cycles. • Biophysical factors directly contributed to SUHI and their seasonal hysteresis. [ABSTRACT FROM AUTHOR]

The persistence of spatial segregation with respect to income and race is well documented. However, assessment of spatial segregation in daily activities is challenging due to the limited availability of human movement data. With the ubiquitous availability of mobile phones and location-based service applications, human movement data has become widely available. It is now possible to explore spatial interactions and assess the extent of social segregation in daily activity spaces. Using Los Angeles County as our case study, we perform a temporal analysis by conducting K-means time-series clustering using mobile phone data to examine social interaction levels among various sociodemographic groups during the COVID-19 pandemic. Selected sociodemographic variables are assessed among the identified time-series clusters. We find a strong association between sociodemographic characteristics and social interaction levels, potentially leading to disparate exposures to the risk from COVID-19. Socially disadvantaged populations tend to be more segregated from other groups in daily activities, and the COVID-19 pandemic increases the disparity. Low-income and ethnic minority populations became more isolated from Whites and the more economically affluent during the COVID-19 pandemic. Policies that aim to encourage social interactions and mitigate segregation effectively should further consider people's sociodemographic variables and relevant neighborhood characteristics. • This study examined potential social interaction across various sociodemographic groups during the COVID-19 pandemic in LA. • The K-means time-series clustering was used to explore temporal changes in social interaction levels during the pandemic. • Exposure to the same group was much greater than exposure to different groups and the race effect was more important. • The socially disadvantaged tended to be more segregated in daily activities, and the pandemic aggravated the disparity. [ABSTRACT FROM AUTHOR]

In recent decades, scientific literature on demographic research is increasing attention on spatial data analysis. It is considered a useful and reliable analysis methodology in evaluating complex regional development processes. However, due to its complexity and reliance on properly captured and quantified spatial relations, it remains a difficult topic for many scholars and practitioners. In Latvia, spatial demographic analysis may prove useful, providing opportunities for uncovering new dimensions of long-term regional demographic and economic development issues. Here, the author analyses spatial distribution aspects of key demographic indicators in Latvia’s municipalities, the associated socio-economic factors and their impact. The implications of the identified spatial processes and dependencies for regional development policy and aid are discussed, including possible lessons learned from or shared with the EU Eastern Partnership countries facing similar challenges.

The combination of transfer learning and remote sensing image processing technology can effectively improve the automation level of image information extraction from a remote sensing time series. However, in the processing of polarimetric synthetic aperture radar (PolSAR) time-series images, the existing transfer learning methods often cannot make full use of the time-series information of the images, relying too much on the labeled samples in the target domain. Furthermore, the speckle noise inherent in synthetic aperture radar (SAR) imagery aggravates the difficulty of the manual selection of labeled samples, so these methods have difficulty in meeting the processing requirements of large data volumes and high efficiency. In lieu of these problems and the spatio-temporal relational knowledge of objects in time-series images, this paper introduces the theory of time-series clustering and proposes a new three-phase time-series clustering algorithm. Due to the full use of the inherent characteristics of the PolSAR images, this algorithm can accurately transfer the labels of the source domain samples to those samples that have not changed in the whole time series without relying on the target domain labeled samples, so as to realize transductive sample label transfer for PolSAR time-series images. Experiments were carried out using three different sets of PolSAR time-series images and the proposed method was compared with two of the existing methods. The experimental results showed that the transfer precision of the proposed method reaches a high level with different data and different objects and it performs significantly better than the existing methods. With strong reliability and practicability, the proposed method can provide a new solution for the rapid information extraction of remote sensing image time series.

Clustering in energy markets is a top topic with high significance on expert and intelligent systems. The main impact of is paper is the proposal of a new clustering framework for the automatic classification of electricity customers’ loads. An automatic selection of the clustering classification algorithm is also highlighted. Finally, new customers can be assigned to a predefined set of clusters in the classification phase. The computation time of the proposed framework is less than that of previous classification techniques, which enables the processing of a complete electric company sample in a matter of minutes on a personal computer. The high accuracy of the predicted classification results verifies the performance of the clustering technique. This classification phase is of significant assistance in interpreting the results, and the simplicity of the clustering phase is sufficient to demonstrate the quality of the complete mining framework. [ABSTRACT FROM AUTHOR]

Recent approaches in load profiling involve the utilization of clustering algorithms to classify a load data set with little or no external information on its structure and relationships between the data is available. In the load profiling literature many algorithms have been presented. K-means is the most commonly used algorithms. While its robustness is already displayed, the main limitation lies on its dependence on the initialization phase. The present paper proposes two novel modified versions of the algorithm in order to deal with the aforementioned problem. Clustering is part of a multi-stage load profiling framework. Apart from the clustering itself, other stages include the selection of pattern representation technique and the extraction of “ representative ” consumers. Apart from the expressing the patterns as time sequences of load values, two other are utilized that refer to a dimensionality reduction method and statistical indexes. The comparison of the algorithms is held through four clustering validity indicators. Simulation results indicate that the proposed modified versions of the K-means lead to higher clustering accuracy in all cases examined. Moreover, the multi-stage clustering approach followed in this study leads to lower clustering time requirements, a fact that is significant in cases with vast amount of data. [ABSTRACT FROM AUTHOR]

The changes and evolution of the electricity distribution has provided new possibilities to the electricity providers for developing a better marketing and trading strategies. A key aspect for designing specific tariff structures is the identification of customers groups exhibiting similar consumption patterns. This paper presents a new methodology for the classification of electricity customers on the basis of their electrical behaviour. Approach is based on the periodicity analysis and well known clustering technique - k-means. The paper presents the classification results obtained on a set of 3753 industrial users, whose consumption has been monitored for 3 years. [ABSTRACT FROM AUTHOR]

Speaker diarization is a problem of separating unknown speakers in a conversation into homogeneous parts in the speaker sense. State-of-the-art diarization systems are based on i-vector methodologies. However, these approaches require large quantities of training data, which must be obtained from an environment that is similar to that of the conversation being diarized. In this paper we present a diarization system that does not require such training data but instead can suffice with some development data for parameter-tuning. This system is a generalization of the well-known hidden Markov model (HMM), a popular clustering algorithm trained by Viterbi statistics. Our proposed model, referred to as a hidden distortion model (HDM), is based on state distortion models and transition costs, for which probabilistic calculations are not mandatory, in contrast to the case of HMM. We provide a mathematical basis for our approach, and we demonstrate that Viterbi-based HMM can be seen as a special case of HDM. This proximity allows us to apply similar approaches for state-model training when the new paradigm is used to learn sequence dependencies. We carry out diarizations of two-speaker telephone conversations in order to evaluate the performance of HDM. When applied to conversations from the LDC CALLHOME database, HDM improves on the performance of a baseline HMM system by about 26% (relative improvement). Moreover, when applied to the NIST 2005 database, it yields a small improvement over the HMM system. [ABSTRACT FROM AUTHOR]

Load profiling refers to a procedure that leads to the formulation of daily load curves and consumer classes regarding the similarity of the curve shapes. This procedure incorporates a set of unsupervised machine learning algorithms. While many crisp clustering algorithms have been proposed for grouping load curves into clusters, only one soft clustering algorithm is utilized for the aforementioned purpose, namely the Fuzzy C-Means (FCM) algorithm. Since the benefits of soft clustering are demonstrated in a variety of applications, the potential of introducing a novel modification of the FCM in the electricity consumer clustering process is examined. Additionally, this paper proposes a novel Demand Side Management (DSM) strategy for load management of consumers that are eligible for the implementation of Real-Time Pricing (RTP) schemes. The DSM strategy is formulated as a constrained optimization problem that can be easily solved and therefore, making it a useful tool for retailers' decision-making framework in competitive electricity markets. [ABSTRACT FROM AUTHOR]

This paper presents a completely data-driven and machine-learning-based approach, in two stages, to first characterize and then forecast hourly water demand in the short term with applications of two different data sources: urban water demand (SCADA data) and individual customer water consumption (AMR data). In the first case, reliable forecasting can be used to optimize operations, particularly the pumping schedule, in order to reduce energy-related costs, while in the second case, the comparison between forecast and actual values may support the online detection of anomalies, such as smart meter faults, fraud or possible cyber-physical attacks. Results are presented for a real case: the water distribution network in Milan. [ABSTRACT FROM AUTHOR]

The importance of Short-Term Load Forecasting (STLF) in power systems planning and management is reflected by the plethora of the related researches. STFL is a popular technical field in the power systems community and already counts many years of research activities and applications. The vast majority of the studies focus at the aggregated system load. Little attention is placed at small size loads, i.e. in buses of the transmission and distribution systems. Since there is a continuous advancement of smart grids technologies involving small size loads, bus STLF is a potentially important tool in smart grid applications. In contrast to system load, bus load presents a high level of stochasticity. Thus a robust forecaster should be able to capture and simulate the attributes of bus loads. The scope of the study is to develop bus forecasting models for day-ahead and hour-ahead load predictions. The models are based on Artificial Neural Networks (ANNs). Using a clustering methodology, the forecasting accuracy of the ANNs is enhanced leading to the formulation of hybrid forecasting models that are characterized by high level of parameterization and efficiency. The developed models are tested on a set of buses covering urban, sub-urban and industrial loads. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) are a favorable scheme in load forecasting applications mainly due to their endogenous capacity of robust modeling of data sets with highly non-linear relationship between inputs and outputs. Usually, the inputs correspond to historical load values, exogenous variables like temperature, day type identification codes and others. The outputs refer to the load values under examination. The majority of the load forecasting related literature focuses in aggregated load system level. While contemporary research efforts focus in smart grid technologies, there is need to study the characteristics of small scaled loads. Bus load forecasting refers to prediction of the demand patterns in buses of the transmission and distribution systems. Bus load exhibits low correlation with the aggregated system load, since it is characterized by a high level of stochasticity. Hence, a proper selection and formulation of the forecasting model is essential in order to keep the prediction accuracy within acceptable ranges. The treatment of bus load characteristics is held with computational intelligence techniques such as clustering and ANN. Neural network based systems are a favorable scheme in recent years in price and load predictions over traditional time series models. ANN can fully adapt expert knowledge and modify their parameters accordingly to simulate the problem`s attributions through training paradigms. Thus, ANN based systems are an essential choice, justified by the paper`s findings, for highly volatile time series. This work focuses on the short-term load forecasting (STLF) of a number of buses within the Greek interconnected system. Firstly, a modified version of the ANN already proposed for the aggregated load of the interconnected system is employed. To enhance the forecasting accuracy of the ANN, the load profiling methodology is used resulting to the formulation of two novel hybrid forecasting models. These models refer to the combination of the ANN with a clustering algorithm, resulting to superior performance. Simulation results indicate that the combination captures and successfully treats the special characteristics of the bus load patterns. The scope of the present paper is to develop efficient forecasting systems for short-term bus load predictions. This is a current research challenge due to the high interest for smart grids and demand side management applications by utilities, regulators, retailer and energy service companies. Bus load forecasting appears to be a more difficult engineering problem compared to forecasting of the total load of a country. No hybrid models for bus load predictions have been presented so far in the literature. Two novel clustering based tools are developed and successfully tested in a number of loads covering different types of electricity consumers and demand levels. [ABSTRACT FROM AUTHOR]

Traditionally, short-term electricity price forecasting has been essential for utilities and generation companies. However, the deregulation of electricity markets created a competitive environment and the introduction of new market participants, such as the retailers and aggregators, whose economic viability and profitability highly depends on the spot market price patterns. The aim of this study is to examine artificial neural network (ANN) based models for Day-ahead price forecasting. Specifically, the models refer to the sole application of ANNs or to hybrid models, where the ANN is combined with clustering algorithm. The training data are clustered in homogenous groups and for each cluster, a dedicated forecaster is employed. The proposed models are characterized by comprehensive operation and by high level of flexibility; different inputs can be taken under consideration and different ANN topologies can be examined. The models are tested on a data set that consists of atypical price patterns and many outliers. This approach makes the price forecasting problem a more challenging task, providing evidence that the proposed models can be considered as useful and robust forecasting tools to the actual needs of market participants, including the traditional generation companies and self-producers, but also the retailers/suppliers and aggregators. [ABSTRACT FROM AUTHOR]

A completely data-driven, fully adaptive self-learning algorithm for water demand forecasting in the short-term and with hourly periodicity is proposed, according to the renewed interest generated by the availability of new technological solutions such as Automatic Metering Readers (AMR), a key enabler of the “Smart Water” paradigm. The approach is based on two sequential stages: at the first stage (time-series clustering) the daily water demand patterns (i.e., time-series of hourly data) are analysed to identify a limited set of typical behaviours. At the second stage Support Vector Machine regression is used to obtain one specific forecasting model (consisting of a regression model for each hour) for each cluster identified at the first stage. The approach has been validated on real data acquired by AMRs deployed on the Italian pilot site of ICeWater, computing the widely adopted error measure MAPE (Mean Absolute Percentage Error). [ABSTRACT FROM AUTHOR]

Load profiling refers to a procedure that leads to the formulation of daily load curves and consumer classes regarding the similarity of the curve shapes. This procedure incorporates a set of unsupervised machine learning algorithms. While many crisp clustering algorithms have been proposed for grouping load curves into clusters, only one soft clustering algorithm is utilized for the aforementioned purpose, namely the Fuzzy C-Means (FCM) algorithm. Since the benefits of soft clustering are demonstrated in a variety of applications, the potential of introducing a novel modification of the FCM in the electricity consumer clustering process is examined. Additionally, this paper proposes a novel Demand Side Management (DSM) strategy for load management of consumers that are eligible for the implementation of Real-Time Pricing (RTP) schemes. The DSM strategy is formulated as a constrained optimization problem that can be easily solved and therefore, making it a useful tool for retailers’ decision-making framework in competitive electricity markets.

This paper presents a completely data-driven and machine-learning-based approach, in two stages, to first characterize and then forecast hourly water demand in the short term with applications of two different data sources: urban water demand (SCADA data) and individual customer water consumption (AMR data). In the first case, reliable forecasting can be used to optimize operations, particularly the pumping schedule, in order to reduce energy-related costs, while in the second case, the comparison between forecast and actual values may support the online detection of anomalies, such as smart meter faults, fraud or possible cyber-physical attacks. Results are presented for a real case: the water distribution network in Milan.

Water Distribution Networks (WDN) are large-scale systems whose management is a complex task, with increasing environmental and socio-economic implications worldwide, and with a renewed considerable attention by all stakeholders: local authorities, regulators, environmental groups and the scientific community. WDM managers need to reliably estimate the water demand in the short-term (typically 1 day ahead), in order to operate their reservoirs and treatment plants appropriately to meet demand while reducing costs, in particular energy-related costs for caption, treatment and pumping. In this paper the authors propose a fully adaptive, data-driven and self-learning approach to forecast short term urban water demand in two stages: i) identifying and characterizing typical daily consumption patterns (based, at least, on hourly demand data) and ii) dynamically generating a set of forecasting models for each typical pattern identified at the previous stage. This schema permits to deal with nonlinear variability of the water demand at different levels, automatically characterizing periodicity (e.g., seasonality) and behaviour-related differences among different types of days and hour of the day. The approach has been validated on the urban water demand data acquired through the SCADA system of the Metropolitana Milanese (MM) partner, the urban water distribution utility in Milan, Italy. Moreover, the approach has been developed in order to work also at individual (customer) level, exploiting the new available technological solutions for smart metering (i.e., Automatic Metering Readers, AMRs) now being installed in MM. [ABSTRACT FROM AUTHOR]

Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage profiles of commercial and industrial customers over 24-h periods and group similar profiles. We tested our method on energy usage data provided by several U.S. power utilities. The results show accurate grouping of accounts similar in their energy usage patterns, and potential for the method to be utilized in energy efficiency programs. [ABSTRACT FROM AUTHOR]