scholarly journals A comparative analysis of patterns of electricity use and flexibility potential of domestic and non-domestic building archetypes through data mining techniques

2021 ◽  
Vol 2042 (1) ◽  
pp. 012021
Author(s):  
Selin Yilmaz ◽  
Jonathan Chambers ◽  
Xiang Li ◽  
Martin K. Patel
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Big Data ◽  
Demand Response ◽  
Large Scale ◽  
Smart Meters ◽  
Use Patterns ◽  
Peak Intensity ◽  
Electricity Use ◽  
Significant Difference

Abstract The large-scale deployment of smart meters has led to significant amount of electricity demand data available, driving it into the realm of Big Data. It is a major challenge to exploit this Big Data in order to characterise electricity use patterns and to support demand response policies. In this paper, we perform a featured-based cluster analysis on nine building archetypes (hospitals, schools, offices, hotels, flats, houses etc.) to identify electricity use patterns. Then, four metrics are developed, which are entropy, load curviness, peak intensity and index of hourly ramp rates, to measure these archetypes’ suitability to be involved in demand response schemes. A significant difference in electricity use patterns between the archetypes is found, as well as among the seasons and days of the week. We present a number of metrics for each archetype to establish which type of archetype should be prioritised for demand response programmes in terms of peak management, ramp rates as well as demand flexibility. A key finding of our study is that households offer more demand flexibility than the non-domestic sector and should therefore be incentivized to participate in dynamic electricity tariffs.

scholarly journals Demand Response Model Development for Smart Households Using Time of Use Tariffs and Optimal Control—The Isle of Wight Energy Autonomous Community Case Study

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 541 ◽  
Author(s):  
Sourav Khanna ◽  
Victor Becerra ◽  
Adib Allahham ◽  
Damian Giaouris ◽  
Jamie M. Foster ◽  
...  
Keyword(s):  
Demand Response ◽  
Large Scale ◽  
Energy Transport ◽  
Model Development ◽  
Software Tool ◽  
Load Flow ◽  
Smart Devices ◽  
Smart Meters ◽  
Isle Of Wight

Residential variable energy price schemes can be made more effective with the use of a demand response (DR) strategy along with smart appliances. Using DR, the electricity bill of participating customers/households can be minimised, while pursuing other aims such as demand-shifting and maximising consumption of locally generated renewable-electricity. In this article, a two-stage optimization method is used to implement a price-based implicit DR scheme. The model considers a range of novel smart devices/technologies/schemes, connected to smart-meters and a local DR-Controller. A case study with various decarbonisation scenarios is used to analyse the effects of deploying the proposed DR-scheme in households located in the west area of the Isle of Wight (Southern United Kingdom). There are approximately 15,000 households, of which 3000 are not connected to the gas-network. Using a distribution network model along with a load flow software-tool, the secondary voltages and apparent-power through transformers at the relevant substations are computed. The results show that in summer, participating households could export up to 6.4 MW of power, which is 10% of installed large-scale photovoltaics (PV) capacity on the island. Average carbon dioxide equivalent (CO2e) reductions of 7.1 ktons/annum and a reduction in combined energy/transport fuel-bills of 60%/annum could be achieved by participating households.

Large scale adverse event data mining for targeted therapies development.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 2538-2538
Author(s):  
Mayur Sarangdhar ◽  
Bruce Aronow ◽  
Anil Goud Jegga ◽  
Brian Turpin ◽  
Erin Haag Breese ◽  
...  
Keyword(s):  
Data Mining ◽  
Clinical Trials ◽  
Adverse Event ◽  
Big Data ◽  
Real World ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Large Scale ◽  
Checkpoint Inhibitors ◽  
Anaplastic Lymphoma

2538 Background: Targeted anti-cancer small molecule drugs & immune therapies have had a dramatic impact in improving outcomes & the approach to clinical trials. Increasingly, regulatory approvals are expedited with small studies designed to identify strong efficacy signals. However, this may limit the extent of safety profiling. The use of large scale/big data meta-analyses can identify novel safety & efficacy signals in "real-world" medical settings. Methods: We used AERSMine, an open-source data mining platform to identify drug toxicity signatures in the FDA’s Adverse Event Reporting System of 8.6 million patients. We identified patients (n = 732,198) who received either traditional and targeted cancer therapy & identified therapy-specific toxicity patterns. Patients were classified based on exposures: anthracyclines (n = 83,179), platinum (117,993), antimetabolites (93,062), alkylators (81,507), antimicrotubule agents (97,726), HER2 inhibitors (40,040), VEGFis (79,144), VEGF-TKis (90,734), multi TKis (34,457), anaplastic lymphoma Kis (7,635), PI3K-AKT-mTOR inhibitors (33,864), Bruton TKis (9,247), MEKis (4,018), immunomodulatory agents (174,810), proteasome inhibitors (44,681), immune checkpoint inhibitors (20,287). Pharmacovigilance metrics [Relative Risks & safety signals] were used to establish statistical correlation & toxicity signatures were differentiated using the Kolmogorov–Smirnov test. Results: To validate the use of the AERSMine to detect AEs, we focused on cardiotoxicity. It identified classic drug associated AEs (e.g. ventricular dysfunction with anthracyclines, HER2is & VEGFis; VEGFi hypertension & vascular toxicity; multi TKIs vascular events). AERSMine also identified recently reported uncommon toxicities of myositis/myocarditis with immune checkpoint inhibitors. It indicated a higher frequency of myositis/myocarditis with combination immune checkpoint therapy, paralleling industry corporate safety databases. These toxicities were reported at higher frequencies in patients > 65 yrs. Conclusions: AERSMine “big data” analyses provide a sensitive tool to detect potential new patterns of AEs simultaneously across multiple clinical trials & in the real-world setting.

scholarly journals Migrating From Data Mining to Big Data Mining

2018 ◽  
Vol 7 (3.4) ◽  
pp. 13
Author(s):  
Gourav Bathla ◽  
Himanshu Aggarwal ◽  
Rinkle Rani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Response Time ◽  
Large Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Mining Algorithm ◽  
Big Data Mining ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Data mining is one of the most researched fields in computer science. Several researches have been carried out to extract and analyse important information from raw data. Traditional data mining algorithms like classification, clustering and statistical analysis can process small scale of data with great efficiency and accuracy. Social networking interactions, business transactions and other communications result in Big data. It is large scale of data which is not in competency for traditional data mining techniques. It is observed that traditional data mining algorithms are not capable for storage and processing of large scale of data. If some algorithms are capable, then response time is very high. Big data have hidden information, if that is analysed in intelligent manner can be highly beneficial for business organizations. In this paper, we have analysed the advancement from traditional data mining algorithms to Big data mining algorithms. Applications of traditional data mining algorithms can be straight forward incorporated in Big data mining algorithm. Several studies have analysed traditional data mining with Big data mining, but very few have analysed most important algortihsm within one research work, which is the core motive of our paper. Readers can easily observe the difference between these algorthithms with  pros and cons. Mathemtics concepts are applied in data mining algorithms. Means and Euclidean distance calculation in Kmeans, Vectors application and margin in SVM and Bayes therorem, conditional probability in Naïve Bayes algorithm are real examples.  Classification and clustering are the most important applications of data mining. In this paper, Kmeans, SVM and Naïve Bayes algorithms are analysed in detail to observe the accuracy and response time both on concept and empirical perspective. Hadoop, Mapreduce etc. Big data technologies are used for implementing Big data mining algorithms. Performace evaluation metrics like speedup, scaleup and response time are used to compare traditional mining with Big data mining.  

Large-Scale Data Mining and Distributed Processing in Big Data Internet

2014 ◽  
Vol 989-994 ◽  
pp. 4594-4597
Author(s):  
Chun Zhi Xing
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Decision Tree ◽  
Large Scale ◽  
Distributed Processing ◽  
Processing Method ◽  
Decision Tree Algorithm ◽  
Data Query ◽  
Large Scale Data ◽  
Scale Data

With the development of Internet, various Internet-based large-scale data are facing increasing competition. With the hope of satisfying the need of data query, it is necessary to use data mining and distributed processing. As a consequence, this paper proposes a large-scale data mining and distributed processing method based on decision tree algorithm.

Data mining with molecular design rules identifies new class of dyes for dye-sensitised solar cells

2014 ◽  
Vol 16 (48) ◽  
pp. 26684-26690 ◽  
Author(s):  
Jacqueline M. Cole ◽  
Kian Sing Low ◽  
Hiroaki Ozoe ◽  
Panagiota Stathi ◽  
Chitoshi Kitamura ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Solar Cells ◽  
Large Scale ◽  
Data Science ◽  
Molecular Design ◽  
Energy Research ◽  
Design Rules ◽  
New Class ◽  
Large Scale Screening

Big data science informs energy research: large-scale screening of crystal structures identifies unforeseen class of dyes for dye-sensitised solar cells.

scholarly journals Applications of Machine Learning in Public Security Information and Resource Management

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhihui Wang ◽  
Jinyu Wang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Warehouse ◽  
Large Scale ◽  
Relevant Information ◽  
Practical Significance ◽  
Public Security ◽  
Data Mining Algorithms ◽  
New Findings ◽  
Security Information

The data mining and big data technologies could be of utmost importance to investigate outbound and case datasets in the police records. New findings and useful information may potentially be obtained through data preprocessing and multidimensional modeling. Public security data is a kind of “big data,” having characteristics like large volume, rapid growth, various structures, large-scale storage, low density, and time sensitiveness. In this paper, a police data warehouse is constructed and a public security information analysis system is proposed. The proposed system comprises two modules: (i) case management and (ii) public security information mining. The former is responsible for the collection and processing of case information. The latter preprocesses the data of major cases that have occurred in the past ten years to create a data warehouse. Then, we use the model to create a data warehouse based on needs. By dividing the measurement values and dimensions, the analysis and prediction of criminals’ characteristics and the case environment realize relationships between them. In the process of mining and processing crime data, data mining algorithms can quickly find out the relevant information in the data. Furthermore, the system can find out relevant trends and laws to detect criminal cases faster than other methods. This can reduce the emergence of new crimes and provide a basis for decision-making in the public security department that has practical significance.

scholarly journals Acceptability of Traditional Chinese Medicine in Chinese People Based on 10-Year's Real World Study With Mutiple Big Data Mining

2022 ◽  
Vol 9 ◽  
Author(s):  
Yan Guo ◽  
Tengjiao Wang ◽  
Wei Chen ◽  
Ted J. Kaptchuk ◽  
Xilian Li ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Real World ◽  
Large Scale ◽  
Point Of View ◽  
Highly Educated ◽  
The Past ◽  
Medical Visits

In the past decades, numerous clinical researches have been conducted to illuminate the effects of traditional Chinese medicine for better inheritance and promotion of it, which are mostly clinical trials designed from the doctor's point of view. This large-scale data mining study was conducted from real-world point of view in up to 10 years' big data sets of Traditional Chinese Medicine (TCM) in China, including both medical visits to hospital and cyberspace and contemporaneous social survey data. Finally, some important and interesting findings appear: (1) More Criticisms vs. More Visits. The intensity of criticism increased by 2.33 times over the past 10 years, while the actual number of visits increased by 2.41 times. (2) The people of younger age, highly educated and from economically developed areas have become the primary population for utilizing TCM, which is contrary to common opinions on the characteristics of TCM users. The discovery of this phenomenon indicates that TCM deserves further study on how it treats illness and maintains health.

Learning Analytics and Education Data Mining in Higher Education

2021 ◽  
pp. 108-126
Author(s):  
Samira ElAtia ◽  
Donald Ipperciel
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Big Data ◽  
Learning Analytics ◽  
Large Scale ◽  
Educational Data Mining ◽  
Current Situation ◽  
Research Perspective ◽  
Education Data ◽  
The Impact

In this chapter, the authors propose an overview on the use of learning analytics (LA) and educational data mining (EDM) in addressing issues related to its uses and applications in higher education. They aim to provide meaningful and substantial answers to how both LA and EDM can advance higher education from a large scale, big data educational research perspective. They present various tasks and applications that already exist in the field of EDM and LA in higher education. They categorize them based on their purposes, their uses, and their impact on various stakeholders. They conclude the chapter by critically analyzing various forecasts regarding the impact that EDM will have on future educational setting, especially in light of the current situation that shifted education worldwide into some form of eLearning models. They also discuss and raise issues regarding fundamentals consideration on ethics and privacy in using EDM and LA in higher education.

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