A method to multi-attribute decision making with picture fuzzy information based on Muirhead mean

The recently proposed picture fuzzy set (PFS) is a powerful tool for handling fuzziness and uncertainty. PFS is character-ized by a positive membership degree, a neutral membership degree, and a negative membership degree, making it more suitable and useful than the intuitionistic fuzzy set (IFS) when dealing with multi-attribute decision making (MADM). The aim of this paper is to develop some aggregation operators for fusing picture fuzzy information. Considering the Muirhead mean (MM) is an aggregation technology which can consider the interrelationship among all aggregated ar-guments, we extend MM to picture fuzzy context and propose a family of picture fuzzy Muirhead mean operators. In addition, we investigate some properties and special cases of the proposed operators. Further, we develop a novel meth-od to MADM in which the attribute values take the form of picture fuzzy numbers (PFNs). Finally, a numerical example is provided to illustrate the validity of the proposed method

EDAS method for multiple criteria group decision making with picture fuzzy information and its application to green suppliers selections

In this paper, we construct picture fuzzy EDAS model based on traditional EDAS (Evaluation based on Distance from Average Solution) model. Firstly, we briefly review the definition of picture fuzzy sets (PFSs) and introduce the score function, accuracy function and operational laws of picture fuzzy numbers (PFNs). Then, we combine traditional EDAS model for MCGDM with PFNs. In our model, it’s more accuracy and effective for considering the conflicting attributes. Finally, a numerical example for green supplier selection has been given to illustrate this new model and some comparisons between EDAS model with PFNs and PFWA, PFWG aggregation operators are also conducted to further illustrate advantages of the new method. First published online 23 August 201

Fuzzy Equivalence on Standard and Rough Neutrosophic Sets and Applications to Clustering Analysis

In this paper, we propose the concept of fuzzy equivalence on standard neutrosophic sets and rough standard neutrosophic sets. We also provide some formulas for fuzzy equivalence on standard neutrosophic sets and rough standard neutrosophic sets. We also apply these formulas for cluster analysis. Numerical examples are illustrated

Data-Driven Control, Modeling, and Forecasting for Residential Solar Power

Distributed solar generation is rising rapidly due to a continuing decline in the cost of solar modules. Most residential solar deployments today are grid-tied, enabling them to draw power from the grid when their local demand exceeds solar generation and feed power into the grid when their local solar generation exceeds demand. The electric grid was not designed to support such decentralized and intermittent energy generation by millions of individual users. This dramatic increase in solar power is placing increasing stress on the grid, which must continue to balance its supply and demand despite the potential for large solar fluctuations. To address the problem, this thesis proposes new data-driven techniques for better controlling, modeling, and forecasting residential solar power. The grid currently exercises no direct control over its connected solar capacity, but instead indirectly controls it by regulating new solar connections. This approach is highly inefficient and wastes much of the grid\u27s potential to transmit solar. Instead, we propose Software-defined Solar-powered (SDS) systems that dynamically regulate solar flow rates into the grid and design an SDS prototype, called SunShade. Specifically, we introduce a new class of Weighted Power Point Tracking (WPPT) algorithms, inspired by Maximum Power Point Tracking (MPPT), capable of dynamically enforcing both hard and relative caps on solar power, which enables the grid to decouple rate control from admission control. In contrast, to avoid grid regulations entirely, homes can also partially or entirely defect from the grid to fully utilize their solar power without restrictions. We present a switching architecture that enables homes to dynamically switch between a local generator, battery, and solar to co-optimize their cost, carbon footprint, switching frequency, and reliability. We introduce switching policies that reveal a tradeoff between solar utilization and reliability, such that higher solar utilization requires more switching, which can lead to lower reliability. Enabling better control of intermittent solar also requires improving solar performance models, which infer solar output based on current environmental conditions. Recent solar models primarily leverage data from ground-based weather stations, which may be far from solar sites and thus inaccurate. In addition, these weather stations report cloud cover---the most important metric for solar modeling---in coarse units of oktas. Instead, we propose developing solar models based on data from a new generation of Geostationary Operational Environmental Satellites (GOES-16 and GOES-17) that began launching in late 2017. We develop physical and machine learning (ML) models for solar performance modeling using both derived data products released by the National Oceanic and Atmospheric Administration (NOAA), as well as the satellites\u27 raw multispectral data. We find that ML-based models using the raw multispectral data are significantly more accurate than both physical models using derived data products, such as Downward Shortwave Radiation (DSR), and prior okta-based solar models. The raw multispectral data is also beneficial since it is available at much higher spatial and temporal resolutions---1km^2 and every 5 minutes---than oktas---25km^2 and every hour. The accuracy of our ML-based models on multispectral data is also better regardless of whether they are locally trained using data only from a particular solar site or globally trained using data from many solar sites. Since global models can be trained once but used anywhere, they can also enable accurate modeling for sites with limited data, e.g., newly installed solar sites. Solar forecasting models, which predict future solar output based on environmental conditions also help in better solar control. Accurate near-term solar forecasts on the order of minutes to an hour are particularly important because homes and the grid must be able to adapt to large sudden changes in solar output. Current solar forecasting techniques, which primarily use Numerical Weather Predictions (NWP) algorithms, mostly leverage physics-based modeling. These physics-based models are most appropriate for forecast horizons on the order of hours to days and not near-term forecasts on the order of minutes to an hour. While there is some recent work on analyzing images from ground-based sky cameras for accurate near-term solar forecasting, it requires installing additional infrastructure. We instead propose a general model for solar nowcasting from abundant and readily available multispectral satellite data using self-supervised learning. Specifically, we develop deep auto-regressive models using convolutional neural networks (CNN) and long short-term memory networks (LSTM) that are globally trained across multiple locations to predict raw future observations of the spatio-temporal data collected by the recently launched GOES-R series of satellites. Our model estimates a location\u27s future solar irradiance based on satellite observations, which we feed to a regression model trained on smaller site-specific solar data to provide near-term solar photovoltaic (PV) forecasts that account for site-specific characteristics

Thunderstorm Tracking and Monitoring on the Basis of Three Dimensional Lightning Data and Conventional and Polarimetric Radar Data

The aim of this work is to assess the benefit of total-lightning information as independent data source for thunderstorm tracking and short-term prediction (nowcasting) of storm evolution. Special focus has been laid on the three-dimensional lightning information and the in-cloud and cloud-to-ground discrimination provided by the lightning detection network LINET. The reliability of the lightning information and its usability for nowcasting purposes have been tested both separately and in combination with other data sources which are commonly used for thunderstorm nowcasting. The new thunderstorm tracker ec-TRAM (tracking and monitoring of electrically charged cells; Meyer et al. (2009)) has been developed to identify, track, and monitor thunderstorms in high temporal and spatial resolution by combining the information of independently tracked convective ground-precipitation cells and lightning-cells to new cell objects. The algorithm builds on the autonomously operating routines rad-TRAM (tracking and monitoring of radar cells; Kober and Tafferner (2009)) and li-TRAM (tracking and monitoring of lightning cells). The latter has also been developed within this work. The new tracking algorithm has been tested based on a thunderstorm data set of more than 500 storm tracks which were recorded by ec-TRAM in southern Germany during summer 2008. It is found that the newly composed cell objects comprehensively describe simple as well as complex thunderstorm structures and the cell tracking method of ec-TRAM proves to be more coherent and stable in comparison with the tracking performances of rad-TRAM and li-TRAM. For two selected thunderstorms the time series of cell parameters monitored by ec-TRAM have been complemented with three-dimensional polarimetric radar data and satellite data to assess how the temporal evolution and parameter correlation of total lightning strokes, hydrometeor formation, ground precipitation patterns, and cloud top temperature can be used to estimate the storm state and predict its development. The parameter evolutions are found to be consistent with the current state of knowledge. A principal life-cycle scheme can be identified for the cell parameters on large time scales. The stronger fluctuating short-term parameter evolutions are found to refl ect the momentary storm dynamic. Based on the lifetime diagrams several warning parameters for subsequent storm events can be suggested. Significant cell parameter correlations, which can be parameterized, are also found in statistical analyses over the complete data set. Strong positive correlations are found between cell extension, discharge frequency, and in-cloud discharge height. Two cell regimes, sharply separated at a specific cell characteristic, can clearly be identified in all correlation diagrams. Interpreted on the basis of previous studies and in terms of the current state of knowledge, it seems most likely that the two cell-regimes refl ect the storm characteristics of different storm organization forms. The parameterized correlation curves could then be used as cell parameterizations in operational nowcasting tools to predict the dynamic evolution, duration, and danger potential of a storm, provided that the storm system can be classified. Finally, it can be concluded that this study demonstrates the usability and the promising potential of total-lightning data as reliable and independent data source for future nowcasting tools

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Renewable Energy Resource Assessment and Forecasting

In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on ‘Renewable Energy Resource Assessment and Forecasting’ aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources

Solar Power System Plaing & Design

Photovoltaic (PV) and concentrated solar power (CSP) systems for the conversion of solar energy into electricity are technologically robust, scalable, and geographically dispersed, and they possess enormous potential as sustainable energy sources. Systematic planning and design considering various factors and constraints are necessary for the successful deployment of PV and CSP systems. This book on solar power system planning and design includes 14 publications from esteemed research groups worldwide. The research and review papers in this Special Issue fall within the following broad categories: resource assessments, site evaluations, system design, performance assessments, and feasibility studies

Informational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework: A review

Fifty years have gone by since the publication of the first paper on clustering based on fuzzy sets theory. In 1965, L.A. Zadeh had published “Fuzzy Sets” [335]. After only one year, the first effects of this seminal paper began to emerge, with the pioneering paper on clustering by Bellman, Kalaba, Zadeh [33], in which they proposed a prototypal of clustering algorithm based on the fuzzy sets theory

Cloud Segmentation and Classification from All-Sky Images Using Deep Learning

For transforming the energy sector towards renewable energies, solar power is regarded as one of the major resources. However, it is not uniformly available all the time, leading to fluctuations in power generation. Clouds have the highest impact on short-term temporal and spatial variability. Thus, forecasting solar irradiance strongly depends on current cloudiness conditions. As the share of solar energy in the electrical grid is increasing, so-called nowcasts (intra-minute to intra-hour forecasts) are beneficial for grid control and for reducing required storage capacities. Furthermore, the operation of concentrating solar power (CSP) plants can be optimized with high resolution spatial solar irradiance data. A common nowcast approach is to analyze ground-based sky images from All-Sky Imagers. Clouds within these images are detected and tracked to estimate current and immediate future irradiance, whereas the accuracy of these forecasts depends primarily on the quality of pixel-level cloud recognition. State-of-the-art methods are commonly restricted to binary segmentation, distinguishing between cloudy and cloudless pixels. Thereby the optical properties of different cloud types are ignored. Also, most techniques rely on threshold-based detection showing difficulties under certain atmospheric conditions. In this thesis, two deep learning approaches are presented to automatically determine cloud conditions. To identify cloudiness characteristics like a free sun disk, a multi-label classifier was implemented assigning respective labels to images. In addition, a segmentation model was developed, classifying images pixel-wise into three cloud types and cloud-free sky. For supervised training, a new dataset of 770 images was created containing ground truth labels and segmentation masks. Moreover, to take advantage of large amounts of raw data, self-supervised pretraining was applied. By defining suitable pretext tasks, representations of image data can be learned facilitating the distinction of cloud types. Two successful techniques were chosen for self-supervised learning: Inpainting- uperresolution and DeepCluster. Afterwards, the pretrained models were fine-tuned on the annotated dataset. To assess the effectiveness of self-supervision, a comparison with random initialization and pretrained ImageNet weights was conducted. Evaluation shows that segmentation in particular benefits from self-supervised learning, improving accuracy and IoU about 3% points compared to ImageNet pretraining. The best segmentation model was also evaluated on binary segmentation. Achieving an overall accuracy of 95.15%, a state-of-the art Clear-Sky-Library (CSL) is outperformed significantly by over 7% points