This document discusses a project called Census2022 that aims to extract value from domestic consumption data from smart meters in a post-census era. It details how smart meter data at high temporal resolution could be aggregated to small geographic areas to generate household statistics and indicators. The document then describes a study conducted with smart meter-like household electricity consumption data from 180 UK homes. Preliminary analysis of load profile indicators showed differences between households of varying sizes and employment statuses. However, more complex models are needed to better predict household characteristics from electricity use alone. Future steps involve accessing larger datasets and creating novel energy-based social indicators.
How to calculate and interpret CPI as it is used in earned value analysis in project management. Download additional slides, videos, and resources at https://www.christiansonjs.com/
Signup for The Free-Range Technologist, a monthly newsletter filled with creative commons resources, useful apps, and lifehacks: https://mailchi.mp/f8f0219bc305/jscott
How to calculate and interpret SPI as it is used in earned value analysis in project management. Download additional slides, videos, and resources at https://www.christiansonjs.com/
Signup for The Free-Range Technologist, a monthly newsletter filled with creative commons resources, useful apps, and lifehacks: https://mailchi.mp/f8f0219bc305/jscott
Formation Produits dérivés et structurés de change: mécanismes et utilisationsActions-Finance
Actions-Finance propose la formation Produits dérivés et structurés de change: mécanismes et utilisations
Cette formation en finance permet notamment de:
A partir d’une connaissance existante du marché de change, développer la compréhension et la pratique des dérivés et structurés de change.
Pour plus de renseignements sur la formation Produits dérivés et structurés de change: mécanismes et utilisations, N’hésitez pas à nous contacter par téléphone au + 33 (0)1 47 20 37 30, ou par email sur contact@actions-finance.com
How to calculate and interpret CPI as it is used in earned value analysis in project management. Download additional slides, videos, and resources at https://www.christiansonjs.com/
Signup for The Free-Range Technologist, a monthly newsletter filled with creative commons resources, useful apps, and lifehacks: https://mailchi.mp/f8f0219bc305/jscott
How to calculate and interpret SPI as it is used in earned value analysis in project management. Download additional slides, videos, and resources at https://www.christiansonjs.com/
Signup for The Free-Range Technologist, a monthly newsletter filled with creative commons resources, useful apps, and lifehacks: https://mailchi.mp/f8f0219bc305/jscott
Formation Produits dérivés et structurés de change: mécanismes et utilisationsActions-Finance
Actions-Finance propose la formation Produits dérivés et structurés de change: mécanismes et utilisations
Cette formation en finance permet notamment de:
A partir d’une connaissance existante du marché de change, développer la compréhension et la pratique des dérivés et structurés de change.
Pour plus de renseignements sur la formation Produits dérivés et structurés de change: mécanismes et utilisations, N’hésitez pas à nous contacter par téléphone au + 33 (0)1 47 20 37 30, ou par email sur contact@actions-finance.com
Hunting for (energy) demanding practices using big & medium sized dataBen Anderson
Presentation given at 'Reshaping the Domestic Nexus: Analytical Insights and Methodologies', Manchester 23/11/2015 (see https://nexusathome.wordpress.com/2015/12/02/workshop-2-reshaping-the-domestic-nexus-manchester/)
Electricity consumption and household characteristics: Implications for censu...Ben Anderson
Presentation given at MRS Workshop "Can Big Data replace the Census? What does Big Data give us now?" , March 7, 2016, MRS, London (https://www.mrs.org.uk/event/conferences/can_big_data_replace_the_census/course/4088/id/10035)
Small Area Estimation as a tool for thinking about temporal and spatial varia...Ben Anderson
Anderson, B (2014) Small Area Estimation as a tool for thinking about temporal and spatial variation in energy demand. Paper presented at AURIN/NATSEM Microsimulation Workshop, University of Melbourne, Thursday 4th December 2014
The Time and Timing of UK Domestic Energy DEMANDBen Anderson
Anderson, B. (2014) The Time and Timing of UK Domestic Energy DEMAND. Keynote paper presented at the 2014 Otago Energy Research Centre Symposium, University of Otago, Dunedin, New Zealand, 28/11/2014.
PRACTICE HUNTING: Time Use Surveys for a quantification of practices distribu...Ben Anderson
Mathieu Durand-Daubin (EDF R&D-ECLEER)
Ben Anderson (Southampton University)
Paper presented at BEHAVE 2014, Said Business School, Oxford, 3rd September 2014
The Rhythms and Components of ‘Peak Energy’ DemandBen Anderson
Ben Anderson – University of Southampton (@dataknut)
Jacopo Torriti – University of Reading
Richard Hanna – University of Reading
Paper presented at BEHAVE 2014, Said Business School, Oxford, 3rd September 2014.
Tracking Social Practices with Big(ish) dataBen Anderson
Paper presented at 'Methodology' session of PRACTICES, THE BUILT ENVIRONMENT AND SUSTAINABILITY EARLY CAREER RESEARCHER NETWORK Workshop,
26-27 June 2014, Cambridge
Do ‘eco’ attitudes & behaviours explain the uptake of domestic energy product...Ben Anderson
"Do ‘eco’ attitudes & behaviours explain the uptake of domestic energy production technologies?"
Paper presented at "What Makes Us Act Green?", June 25 2014, London
Small Area Estimation as a tool for thinking about spatial variation in energ...Ben Anderson
Paper presented at "Spatial Variation in Energy Use, Attitudes and Behaviours: Implications for Smart Grids and Energy Demand", Policy Studies Institute, Friday, 7 February 2014, London, United Kingdom
The Distribution of Domestic Energy-Tech in Great Britain: 2008 – 2011Ben Anderson
Paper presented at 'What makes us act green?', Research & Policy Seminar, 17th December 2013, BIS Conference Centre, London. Uses @usociety survey data to analyse household uptake of solar PV and solar thermal in the UK 2008-2011
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Hunting for (energy) demanding practices using big & medium sized dataBen Anderson
Presentation given at 'Reshaping the Domestic Nexus: Analytical Insights and Methodologies', Manchester 23/11/2015 (see https://nexusathome.wordpress.com/2015/12/02/workshop-2-reshaping-the-domestic-nexus-manchester/)
Electricity consumption and household characteristics: Implications for censu...Ben Anderson
Presentation given at MRS Workshop "Can Big Data replace the Census? What does Big Data give us now?" , March 7, 2016, MRS, London (https://www.mrs.org.uk/event/conferences/can_big_data_replace_the_census/course/4088/id/10035)
Small Area Estimation as a tool for thinking about temporal and spatial varia...Ben Anderson
Anderson, B (2014) Small Area Estimation as a tool for thinking about temporal and spatial variation in energy demand. Paper presented at AURIN/NATSEM Microsimulation Workshop, University of Melbourne, Thursday 4th December 2014
The Time and Timing of UK Domestic Energy DEMANDBen Anderson
Anderson, B. (2014) The Time and Timing of UK Domestic Energy DEMAND. Keynote paper presented at the 2014 Otago Energy Research Centre Symposium, University of Otago, Dunedin, New Zealand, 28/11/2014.
PRACTICE HUNTING: Time Use Surveys for a quantification of practices distribu...Ben Anderson
Mathieu Durand-Daubin (EDF R&D-ECLEER)
Ben Anderson (Southampton University)
Paper presented at BEHAVE 2014, Said Business School, Oxford, 3rd September 2014
The Rhythms and Components of ‘Peak Energy’ DemandBen Anderson
Ben Anderson – University of Southampton (@dataknut)
Jacopo Torriti – University of Reading
Richard Hanna – University of Reading
Paper presented at BEHAVE 2014, Said Business School, Oxford, 3rd September 2014.
Tracking Social Practices with Big(ish) dataBen Anderson
Paper presented at 'Methodology' session of PRACTICES, THE BUILT ENVIRONMENT AND SUSTAINABILITY EARLY CAREER RESEARCHER NETWORK Workshop,
26-27 June 2014, Cambridge
Do ‘eco’ attitudes & behaviours explain the uptake of domestic energy product...Ben Anderson
"Do ‘eco’ attitudes & behaviours explain the uptake of domestic energy production technologies?"
Paper presented at "What Makes Us Act Green?", June 25 2014, London
Small Area Estimation as a tool for thinking about spatial variation in energ...Ben Anderson
Paper presented at "Spatial Variation in Energy Use, Attitudes and Behaviours: Implications for Smart Grids and Energy Demand", Policy Studies Institute, Friday, 7 February 2014, London, United Kingdom
The Distribution of Domestic Energy-Tech in Great Britain: 2008 – 2011Ben Anderson
Paper presented at 'What makes us act green?', Research & Policy Seminar, 17th December 2013, BIS Conference Centre, London. Uses @usociety survey data to analyse household uptake of solar PV and solar thermal in the UK 2008-2011
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
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Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Business update Q1 2024 Lar España Real Estate SOCIMI
Census2022: Extracting value from domestic consumption data in a postcensus era
1. Census2022: Extracting value from
domestic consumption data in a postcensus
era
BEHAVE conference – September 2014
Andy Newing a.newing@soton.ac.uk
Ben Anderson b.anderson@soton.ac.uk (@dataknut)
Sustainable Energy Research Group
2. Census2022: Extracting value from domestic… BEHAVE Sept 2014
What we are trying to do: Census2022
UK Census 2011/2021 evolution
Timeliness & cost
Challenges
Finding new ways to deliver the Census – ‘Census-like’
Opportunities
New kinds of data
New kinds of social indicators - ‘Census-plus’
More frequently
2
3. Census2022: Extracting value from domestic… BEHAVE Sept 2014
Smart metering
• Universal mandate
• Geo-coded
• Doesn’t ‘lie’
• (but may be errors/’missing’)
• High temporal resolution
• Near 100% coverage
• Especially for electricity
3
Crucial!!
4. Census2022: Extracting value from domestic… BEHAVE Sept 2014
Generating area based household
statistics and indicators
Household Load Profiles
Infer household characteristics
Aggregate to small area geographies
6. Census2022: Extracting value from domestic… BEHAVE Sept 2014
UoS Energy Monitoring Study (UoS-E)
6
Smart meter-like household dataset
n=180
Repeated surveys:
characteristics, behaviors and attitudes
1 second level power import
Sample: October 2011
~ 500m records (1 second)
Cleaned & checked
Aggregated (mean power)
~ 250,000 records (half hourly)
7. Census2022: Extracting value from domestic… BEHAVE Sept 2014
7
Descriptive Analysis
1-2 persons vs 3+ Midweek: No children vs 1-2 vs 3+
Midweek: Respondent in
employment vs not
9. Census2022: Extracting value from domestic… BEHAVE Sept 2014
Evening consumption factor (ECF)
Midweek (Tuesday – Thursday)
Ratio of mean 30
minute evening
peak power
import (4pm –
8pm) to off peak
power import
Ψ note: n= 5
9
ECF All households Employed Not in active
employment
All households 2.13 1.64
No Children 2.21 2.54 2.09
With Children 2.31 2.29 1.30Ψ
10. Census2022: Extracting value from domestic… BEHAVE Sept 2014
Predicting household characteristics
• Exploratory linear regression
Presumption of
availability via
administrative sources
• clear links but low explanatory power
11. Census2022: Extracting value from domestic… BEHAVE Sept 2014
Conclusions & Next Steps
• Value of pure load profile approaches unclear
• More complex regression models needed
• Exploration of ‘time series’approaches
• Need:
• Access to larger dataset with greater range of household
types
• Creation of ‘census-plus’ indicators
• Novel energy consumption-based social indicators?
12. Census2022: Extracting value from domestic… BEHAVE Sept 2014
Thank you
http://www.energy.soton.ac.uk/category/research/energy-behaviour/
census-2022/
Ben Anderson b.anderson@soton.ac.uk (@dataknut)
Andy Newing a.newing@soton.ac.uk
12