Automatic fault detection for Building Integrated Photovoltaic (BIPV) systems using time series methods

2018 ◽  
Vol 8 (2) ◽  
pp. 160-170 ◽  
Author(s):  
Mohsen Shahandashti ◽  
Baabak Ashuri ◽  
Kia Mostaan
Keyword(s):  
Time Series ◽  
Automatic Detection ◽  
Cost Effective ◽  
Performance Ratio ◽  
Solar Irradiation ◽  
Short Term ◽  
Content Type ◽  
Structure Changes ◽  
Cost Effective Approach ◽  
Building Integrated Photovoltaic

PurposeFaults in the actual outdoor performance of Building Integrated Photovoltaic (BIPV) systems can go unnoticed for several months since the energy productions are subject to significant variations that could mask faulty behaviors. Even large BIPV energy deficits could be hard to detect. The purpose of this paper is to develop a cost-effective approach to automatically detect faults in the energy productions of BIPV systems using historical BIPV energy productions as the only source of information that is typically collected in all BIPV systems.Design/methodology/approachEnergy productions of BIPV systems are time series in nature. Therefore, time series methods are used to automatically detect two categories of faults (outliers and structure changes) in the monthly energy productions of BIPV systems. The research methodology consists of the automatic detection of outliers in energy productions, and automatic detection of structure changes in energy productions.FindingsThe proposed approach is applied to detect faults in the monthly energy productions of 89 BIPV systems. The results confirm that outliers and structure changes can be automatically detected in the monthly energy productions of BIPV systems using time series methods in presence of short-term variations, monthly seasonality, and long-term degradation in performance.Originality/valueUnlike existing methods, the proposed approach does not require performance ratio calculation, operating condition data, such as solar irradiation, or the output of neighboring BIPV systems. It only uses the historical information about the BIPV energy productions to distinguish between faults and other time series properties including seasonality, short-term variations, and degradation trends.

The Pransky interview: Tony (Harvey) Koselka, Co-founder and Chief Operating Officer, Vision Robotics Corporation

2020 ◽  
Vol 47 (2) ◽  
pp. 147-151
Author(s):  
Joanne Pransky
Keyword(s):  
Industrial Robot ◽  
Cost Effective ◽  
Content Type ◽  
Chief Operating Officer ◽  
Award Winning ◽  
Start Up ◽  
Cost Effective Approach ◽  
Tree Fruit ◽  
Catch Up ◽  
Estimation System

Purpose The following paper is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a robotic industry engineer-turned-innovator regarding the challenges of bringing technological discoveries to fruition. The paper aims to discuss these issues. Design/methodology/approach The interviewee is Tony Koselka, Co-founder and Chief Operating Officer, Vision Robotics Corporation (VRC). In this interview, Koselka shares how he first got started in the robotics field along with his experiences in running his start-up. Findings Koselka earned a bachelor’s and a master’s degree in mechanical engineering from MIT. Prior to his work at VRC, he successfully co-founded and sold his first start-up, CyberGear. VRC was formed in 1999 with the goal to develop autonomous robotic solutions that focused on vision-based mapping. With strategic partners, the team began work on consumer applications that enabled the company to create a uniquely robust and cost-effective approach to robotics. It entered the agricultural market in 2004 with a feasibility study for harvesting oranges. While waiting for picking-hand technology to catch up with the machine vision, the initial concept transformed into a deployable crop-load estimation system for tree fruit. In 2011, VRC was approached by lettuce growers to develop a lettuce thinner. In a few months, the company built its lettuce thinner, which was released in 2012 and since then has collectively thinned hundreds of thousands of acres. Originality/value Koselka is an award-winning design engineer who holds 21 US patents. He has managed the design of all the mechanical systems of his start-up, VRC, including those on the lettuce thinner and the grapevine pruner, which enable robots to intelligently and accurately perform myriad tasks, including pruning, weeding and thinning.

Collection analysis on social work: a cost-effective approach

2015 ◽  
Vol 34 (2) ◽  
pp. 59-64
Author(s):  
Maggie Liu
Keyword(s):  
Social Work ◽  
Cost Effective ◽  
Academic Departments ◽  
Content Type ◽  
Subject Knowledge ◽  
Internal Resources ◽  
Cost Effective Approach ◽  
Depth Study ◽  
Subject Areas ◽  
Development And Management

Purpose – The purpose of this paper is to ensure the provision of effective library support to the scholarly community in areas of collection development and management. Last year, the author’s library made the first attempt to conduct a collection evaluation on a multidisciplinary subject – social work. Design/methodology/approach – In view of extensive subject coverage of a cross-disciplinary subject and the library’s constraints, a cost-effective and manageable strategy using internal resources was used. The focus of the study was to concentrate on core subject areas of social work. By making use of circulation statistics of those areas, adequacy, strengths and weaknesses of the collection would be identified. Findings – A positive adequacy of the social work collection was evidenced from a high usage of the collection. An in-depth study on the two major active sections, DDC 361 (social problems and services) and DDC 362 (social work for different groups of people), was also undertaken. Specific subject areas for improvement were identified. Originality/value – Through conducting the project, it not only directly served the main purpose of enhancing the quality of the collection in alliance with the development of academic departments, but also expanded our subject knowledge. It is hoped that our experience can offer tips and stimulant for other libraries contemplating collection analysis on a multidisciplinary subject under restricted resources.

Grey system based novel approach for stock market forecasting

2015 ◽  
Vol 5 (2) ◽  
pp. 178-193 ◽  
Author(s):  
R.M. Kapila Tharanga Rathnayaka ◽  
D.M.K.N Seneviratna ◽  
Wei Jianguo
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Stock Exchange ◽  
Moving Average ◽  
Grey Prediction ◽  
Grey System ◽  
Short Term ◽  
Content Type ◽  
Stock Market Forecasting ◽  
Novel Approach

Purpose – Making decisions in finance have been regarded as one of the biggest challenges in the modern economy today; especially, analysing and forecasting unstable data patterns with limited sample observations under the numerous economic policies and reforms. The purpose of this paper is to propose suitable forecasting approach based on grey methods in short-term predictions. Design/methodology/approach – High volatile fluctuations with instability patterns are the common phenomenon in the Colombo Stock Exchange (CSE), Sri Lanka. As a subset of the literature, very few studies have been focused to find the short-term forecastings in CSE. So, the current study mainly attempted to understand the trends and suitable forecasting model in order to predict the future behaviours in CSE during the period from October 2014 to March 2015. As a result of non-stationary behavioural patterns over the period of time, the grey operational models namely GM(1,1), GM(2,1), grey Verhulst and non-linear grey Bernoulli model were used as a comparison purpose. Findings – The results disclosed that, grey prediction models generate smaller forecasting errors than traditional time series approach for limited data forecastings. Practical implications – Finally, the authors strongly believed that, it could be better to use the improved grey hybrid methodology algorithms in real world model approaches. Originality/value – However, for the large sample of data forecasting under the normality assumptions, the traditional time series methodologies are more suitable than grey methodologies; especially GM(1,1) give some dramatically unsuccessful results than auto regressive intergrated moving average in model pre-post stage.

554: A Safe and a Cost-Effective Approach for Managing Patients with Levitronix CentriMag® Short Term Devices

2010 ◽  
Vol 29 (2) ◽  
pp. S180-S180
Author(s):  
A. Firouzi ◽  
R.S. George ◽  
K. Absalom ◽  
S.M. Panther ◽  
H. Doyle ◽  
...  
Keyword(s):  
Cost Effective ◽  
Short Term ◽  
Cost Effective Approach

Behavior analysis methods for Twitter users based on transitions in posting activities

2014 ◽  
Vol 10 (4) ◽  
pp. 363-377 ◽  
Author(s):  
Yutaro Yamaguchi ◽  
Shuhei Yamamoto ◽  
Tetsuji Satoh
Keyword(s):  
Time Series ◽  
State Transition ◽  
User Behavior ◽  
Text Messages ◽  
Analysis Method ◽  
Short Term ◽  
Content Type ◽  
User Behaviors ◽  
Analysis Methods ◽  
Twitter Users

Purpose – The purpose of this paper is to activate latent users posts by modeling user behaviors by a transition of clusters that represent particular posting activities. Twitter has rapidly spread and become an easy and convenient microblog that enables users to exchange instant text messages called tweets. There are so many latent users whose posting activities have decreased. Design/methodology/approach – Under this model, two kinds of time-series analysis methods are proposed to clarify the lifecycles of Twitter users. In the first one, all users belong to a cluster consisting of several features at individual time slots and move among the clusters in a time series. In the second one, the posting activities of Twitter users are analyzed by the amount of tweets that vary with time. Findings – This sophisticated evaluation using a large actual tweet-set demonstrated the proposed methods effectiveness. The authors found a big difference in the state transition diagrams between long- and short-term users. Analysis of short-term users introduces effective knowledge for encouraging continued Twitter use. Originality/value – An the efficient user behavior model, which describes transitions of posting activities, is proposed. Two kinds of time longitudinal analysis method are evaluated using a large amount of actual tweets.

Assessment of service quality using text mining – evidence from private sector banks in India

2018 ◽  
Vol 36 (4) ◽  
pp. 594-615 ◽  
Author(s):  
Somnath Chakrabarti ◽  
Deepak Trehan ◽  
Mayank Makhija
Keyword(s):  
Service Quality ◽  
Sentiment Analysis ◽  
Banking Sector ◽  
Cost Effective ◽  
Retail Banking ◽  
Traditional Concept ◽  
Content Type ◽  
Product Categories ◽  
Cost Effective Approach ◽  
Private Banks

Purpose As the retail banking institutions are becoming more customer centric, their focus on service quality is increasing. Established service quality frameworks such as SERVQUAL and SERVPERF have been applied in the banking sector. While these models are widely accepted, they are expensive because of the need for replication across bank branches. The purpose of this paper is to propose a novel, user friendly and cost effective approach by amalgamating the traditional concept of service quality in banks (marketing base) and sentiment analysis literature (information systems base). Design/methodology/approach In this study, the main objective is to analyze user reviews to better understand the correlation between RATER dimension sentiment scores as independent variables and user overall rating (customer satisfaction) grouping in “good” and “bad” as dependent variable through development of authors’ own logistic regression model using lexicon-based sentiment analysis. The model has been developed for three largest private banks in India pertaining to three banking product categories of loans, savings and current accounts and credit cards. Findings The results show that the responsiveness and tangibles dimensions significantly impact the user evaluation rating. Even though the three largest private banks in India are concentrating on the tangibles dimension, not all of them are sufficiently focused on the responsiveness dimension. Additionally, customers looking for loan products are more susceptible to negative perceptions on service quality. Originality/value This study has highlighted two types of scores whereby user provided overall evaluation scores help provide validation to the sentiment scores. The developed model can be used to assess performance of a bank in comparison to its peers and to generate in depth insights on point of parity (POP) and point of difference (POD) fronts.

Forecasting container throughput with long short-term memory networks

2019 ◽  
Vol 120 (3) ◽  
pp. 425-441 ◽  
Author(s):  
Sonali Shankar ◽  
P. Vigneswara Ilavarasan ◽  
Sushil Punia ◽  
Surya Prakash Singh
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Moving Average ◽  
Short Term ◽  
Error Matrix ◽  
Term Memory ◽  
Content Type ◽  
Forecasting Performance ◽  
Long Short Term Memory

Purpose Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods. Design/methodology/approach In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analysis. The forecasting performance of the LSTM model is compared with seven different time-series forecasting methods, namely, autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt–Winter’s, error-trend-seasonality, trigonometric regressors (TBATS), neural network (NN) and ARIMA + NN. The relative error matrix is used to analyze the performance of the different models with respect to bias, accuracy and uncertainty. Findings The results showed that LSTM outperformed all other benchmark methods. From a statistical perspective, the Diebold–Mariano test is also conducted to further substantiate better forecasting performance of LSTM over other counterpart methods. Originality/value The proposed study is a contribution to the literature on the container throughput forecasting and adds value to the supply chain theory of forecasting. Second, this study explained the architecture of the deep-learning-based LSTM method and discussed in detail the steps to implement it.

Classification of challenges in 3D printing for combined electrochemical and microfluidic applications: a review

2019 ◽  
Vol 25 (7) ◽  
pp. 1328-1346 ◽  
Author(s):  
Arivarasi A. ◽  
Anand Kumar
Keyword(s):  
Manufacturing Sector ◽  
Three Dimensional ◽  
Cost Effective ◽  
Scientific Output ◽  
Integrated Process ◽  
Content Type ◽  
Three Dimensional Printing ◽  
Cost Effective Approach ◽  
Dimensional Printing ◽  
Process Material

Purpose The purpose of this paper is to describe, review, classify and analyze the current challenges in three-dimensional printing processes for combined electrochemical and microfluidic fabrication areas, which include printing devices and sensors in specified areas. Design/methodology/approach A systematic review of the literature focusing on existing challenges is carried out. Focused toward sensors and devices in electrochemical and microfluidic areas, the challenges are oriented for a discussion exploring the suitability of printing varied geometries in an accurate manner. Classifications on challenges are based on four key categories such as process, material, size and application as the printer designs are mostly based on these parameters. Findings A key three-dimensional printing process methodologies have their unique advantages compared to conventional printing methods, still having the challenges to be addressed, in terms of parameters such as cost, performance, speed, quality, accuracy and resolution. Three-dimensional printing is yet to be applied for consumer usable products, which will boost the manufacturing sector. To be specific, the resolution of printing in desktop printers needs improvement. Printing scientific products are halted with prototyping stages. Challenges in three-dimensional printing sensors and devices have to be addressed by forming integrated processes. Research limitations/implications The research is underway to define an integrated process-based on three-dimensional Printing. The detailed technical details are not shared for scientific output. The literature is focused to define the challenges. Practical implications The research can provide ideas to business on innovative designs. Research studies have scope for improvement ideas. Social implications Review is focused on to have an integrated three-dimensional printer combining processes. This is a cost-oriented approach saving much of space reducing complexity. Originality/value To date, no other publication reviews the varied three-dimensional printing challenges by classifying according to process, material, size and application aspects. Study on resolution based data is performed and analyzed for improvements. Addressing the challenges will be the solution to identify an integrated process methodology with a cost-effective approach for printing macro/micro/nano objects and devices.

Taylor series approximation and unbiased GM(1,1) based hybrid statistical approach for forecasting daily gold price demands

2019 ◽  
Vol 9 (1) ◽  
pp. 5-18 ◽  
Author(s):  
R.M. Kapila Tharanga Rathnayaka ◽  
D.M.K.N. Seneviratna
Keyword(s):  
Time Series ◽  
Taylor Series ◽  
Statistical Approach ◽  
Time Series Data ◽  
Gold Price ◽  
Series Data ◽  
Short Term ◽  
Content Type ◽  
Taylor Series Approximation ◽  
Series Approximation

Purpose The time series analysis is an essential methodology which comprises the tools for analyzing the time series data to identify the meaningful characteristics for making future ad-judgments. The purpose of this paper is to propose a Taylor series approximation and unbiased GM(1,1) based new hybrid statistical approach (HTS_UGM(1,1)) for forecasting time series data under the poor, incomplete and uncertain information systems in a short period of time manner. Design/methodology/approach The gray forecasting is a dynamical methodology which can be classified into different categories based on their respective functions. The new proposed methodology is made up of three different methodologies including the first-order unbiased GM(1,1), Markov chain and Taylor approximation. In addition to that, two different traditional gray operational mechanisms include GM(1,1) and unbiased GM(1,1) used as the comparisons. The main objective of this study is to forecast gold price demands in a short-term manner based on the data which were taken from the Central Bank of Sri Lanka from October 2017 to December 2017. Findings The error analysis results suggested that the new proposed HTS_UGM(1,1) is highly accurate (less than 10 percent) with lowest RMSE error values in a one head as well as weakly forecasting’s than separate gray forecasting methodologies. Originality/value The findings suggested that the new proposed hybrid approach is more suitable and effective way for forecasting time series indices than separate time series forecasting methodologies in a short-term manner.

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