9,669 research outputs found

    Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation

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    We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into KK clusters and represents each cluster by a simple prototype (e.g., piecewise constant). The total number of segments in the prototypes, PP, is chosen by the user and optimally distributed among the clusters via two dynamic programming algorithms. The practical relevance of the method is shown on two real world datasets

    Data-driven approach to machine condition prognosis using least square regression trees

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    Machine fault prognosis techniques have been considered profoundly in the recent time due to their profit for reducing unexpected faults or unscheduled maintenance. With those techniques, the working conditions of components, the trending of fault propagation, and the time-to-failure are forecasted precisely before they reach the failure thresholds. In this work, we propose an approach of Least Square Regression Tree (LSRT), which is an extension of the Classification and Regression Tree (CART), in association with one-step-ahead prediction of time-series forecasting technique to predict the future conditions of machines. In this technique, the number of available observations is firstly determined by using Cao’s method and LSRT is employed as prognosis system in the next step. The proposed approach is evaluated by real data of low methane compressor. Furthermore, the comparison between the predicted results of CART and LSRT are carried out to prove the accuracy. The predicted results show that LSRT offers a potential for machine condition prognosis

    Artificial Neural Networks for Short-Term Load Forecasting in Microgrids Environment Energy

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    The adaptation of energy production to demand has been traditionally very important for utilities in order to optimize resource consumption. This is especially true also in microgrids where many intelligent elements have to adapt their behaviour depending on the future generation and consumption conditions. However, traditional forecasting has been performed only for extremely large areas, such as nations and regions. This work aims at presenting a solution for short-term load forecasting (STLF) in microgrids, based on a three-stage architecture which starts with pattern recognition by a self-organizing map (SOM), a clustering of the previous partition via k-means algorithm, and finally demand forecasting for each cluster with a multilayer perceptron. Model validation was performed with data from a microgrid-sized environment provided by the Spanish company Iberdrola. (C) 2014 Elsevier Ltd. All rights reserved.Hernandez, L.; Baladron, C.; Aguiar, JM.; Carro, B.; Sanchez-Esguevillas, A.; Lloret, J. (2014). Artificial Neural Networks for Short-Term Load Forecasting in Microgrids Environment Energy. Energy. 75:252-264. doi:10.1016/j.energy.2014.07.065S2522647

    From Smart Meter Data to Pricing Intelligence -- Visual Data Mining towards Real-Time BI

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    The deployment of smart metering in the electricity industry has opened up the opportunity for real-time BI-enabled innovative business applications, such as demand response. Taking a holistic view of BI, this study introduced a visual data mining driven application in order to exemplify the potentials of real-time BI to the electricity businesses. The empirical findings indicate that such an application is capable of extracting actionable insights about customer’s electricity consumption patterns, which will lead to turn timely measured data into pricing intelligence. Based on the findings, we proposed a real-time BI framework, and discussed how it will facilitate the formulation of strategic initiatives for transforming the electricity utility towards sustainable growth. Our research is conducted by following the design science research paradigm. By addressing an emerging issue in the problem domain, it adds empirical knowledge to the BI research landscape

    Smart Water: Short-Term Forecasting Application in Water Utilities

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    The unyielding interconnection between water and energy has made demand forecasting a necessity for water utilities. Electricity prices driven by the time of use has impelled water utilities towards short-term water demand forecasting. The progressive new Smart Water Grid platform has helped water utilities in utilizing their Water Distribution Networks. This two-way platform has provided developers and decision makers with robust models that rely on consumer feedback. Among these models is the water demand forecasting models. Multitudinous demand forecasting methods have been developed but none have utilized model implementation practicality. Utilities differ in size, capacity, and interest. While small size utilities focus on model simplicity, larger utilities prioritize model accuracy. This work focuses on a water utility located in Essex County, Ontario, Canada. This study presents three papers that focus on investigation and evaluation of short-term water demand forecasting techniques. The first paper compares water usage between two crops (tomatoes and bell peppers) in an effort to evaluate a crop to crop forecast technique that relies on one crops watering data in order to produce forecasts for another crop, The second paper examines the effect of model type, input type, and data size on model performance and computational load. The third paper proposes a new methodology where model performance is not sacrificed for model simplification

    A Proposed Intelligent Model with Optimization Algorithm for Clustering Energy Consumption in Public Buildings

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    Abdelaziz, A., Santos, V., & Dias, M. S. (2023). A Proposed Intelligent Model with Optimization Algorithm for Clustering Energy Consumption in Public Buildings. International Journal of Advanced Computer Science and Applications, 14(9), 136-152. [15]. https://doi.org/10.14569/IJACSA.2023.0140915 --- This work has been supported by Portuguese funds through FCT-Fundação para a Ciência e Tecnologia, Instituto Público (IP), under the project FCT UIDB/04466/2020 by Information Sciences and Technologies and Architecture Research Center (ISTAR-IUL), and this work has also been supported by Information Management Research Center (MagIC)-Information Management School of NOVA University LisbonRecently, intelligent applications gained a significant role in the energy management of public buildings due to their ability to enhance energy consumption performance. Energy management of these buildings represents a big challenge due to their unexpected energy consumption characteristics and the deficiency of design guidelines for energy efficiency and sustainability solutions. Therefore, an analysis of energy consumption patterns in public buildings becomes necessary. This reveals the significance of understanding and classifying energy consumption patterns in these buildings. This study seeks to find the optimal intelligent technique for classifying energy consumption of public buildings into levels (e.g., low, medium, and high), find the critical factors that influence energy consumption, and finally, find the scientific rules (If-Then rules) to help decision-makers for determining the energy consumption level in each building. To achieve the objectives of this study, correlation coefficient analysis was used to determine critical factors that influence on energy consumption of public buildings; two intelligent models were used to determine the number of clusters of energy consumption patterns which are Self Organizing Map (SOM) and Batch-SOM based on Principal Component Analysis (PCA). SOM outperforms Batch-SOM in terms of quantization error. The quantization error of SOM and Batch-SOM is 8.97 and 9.24, respectively. K-means with a genetic algorithm were used to predict cluster levels in each building. By analyzing cluster levels, If-Then rules have been extracted, so needs that decision-makers determine the most energyconsuming buildings. In addition, this study helps decisionmakers in the energy field to rationalize the consumption of occupants of public buildings in the times that consume the most energy and change energy suppliers to those buildings.publishersversionpublishe
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