The document presents an optimization-based algorithm for energy disaggregation from low frequency smart meter data. The algorithm formulates energy disaggregation as a least squares minimization problem with penalty terms to impose piecewise constant appliance usage profiles. The objective function fits aggregate consumption while penalizing frequent on/off switching. Constraints ensure each appliance operates at one level per time interval, maximum energy usage over time is not exceeded, and minimum daily usage is satisfied on activation days. The algorithm aims to address challenges of ultra-low frequency smart meter data for energy disaggregation.
Maximum Entropy Reinforcement Learning (Stochastic Control)Dongmin Lee
I reviewed the following papers.
- T. Haarnoja, et al., “Reinforcement Learning with Deep Energy-Based Policies", ICML 2017
- T. Haarnoja, et al., “Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor", ICML 2018
- T. Haarnoja, et al., “Soft Actor-Critic Algorithms and Applications", arXiv preprint 2018
Thank you.
Maximum Entropy Reinforcement Learning (Stochastic Control)Dongmin Lee
I reviewed the following papers.
- T. Haarnoja, et al., “Reinforcement Learning with Deep Energy-Based Policies", ICML 2017
- T. Haarnoja, et al., “Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor", ICML 2018
- T. Haarnoja, et al., “Soft Actor-Critic Algorithms and Applications", arXiv preprint 2018
Thank you.
transmission versus distribution planning, long term versus short term planning,issues in transmission planning,generation planning,capacity resource planning, transmission planning,national and regional planning, integrated resource planning
SAIEE presentation - Power System Resilience - Why should we CARE as energy u...Malcolm Van Harte
Presentation on the latest Power System Resilience thinking:
Thanks to the SAIEE load research chapter for the opportunity to share the interesting work from CIGRE C4.47 WG - Power System Resilience Working Group.
Key questions addressed:
1. What is Power System Resilience?
2. What are the principles?
3. Why is it important to network planners?
4. How does one go about it?
View WEBINAR on Dropbox link here:
https://www.dropbox.com/s/j87z1ljmj55uddx/20200421%20Network%20resilience%20M%20V%20Harte.mp4?dl=0
Design and Analysis of PID and Fuzzy-PID Controller for Voltage Control of DC...Francisco Gonzalez-Longatt
DC microgrids are desired to provide the electricity for the remote areas which are far from the main grid. The microgrid creates the open horizontal environment to interconnect the distributed generation especially photovoltaic (PV). The stochastic nature of the PV output power introduces the large fluctuations of the power and voltage in the microgrid and forced to introduce the controller for voltage stability. There are many control strategies to control the voltage of a DC microgrid in the literature. In this paper the proportional-integral-derivative (PID) and fuzzy logic PID (FL-PID) controller has been designed and compared in term of performance. Performance measures like maximum overshoot and settling time of FL-PID compared with the PID proved that the former is better controller. The controllers are designed and simulated in the MATLAB programming environment. The controllers has been tested for the real time data obtained from Pecan Street Project, University of Texas at Austin USA.
Low-cost convolutional neural network for tomato plant diseases classificationIAESIJAI
Agriculture is a crucial element to build a strong economy, not only because
of its importance in providing food, but also as a source of raw materials for
industry as well as source of energy. Different diseases affect plants, which
leads to decrease in productivity. In recent years, developments in
computing technology and machine-learning algorithms (such as deep neural
networks) in the field of agriculture have played a great role to face this
problem by building early detection tools. In this paper, we propose an
automatic plant disease classification based on a low complexity
convolutional neural network (CNN) architecture, which leads to faster online classification. For the training process, we used more than one 57,000
tomato leaf images representing nine classes, taken under natural
environment, and considered during training without background
subtraction. The designed model achieves 97.04% classification accuracy
and less than 0.2 error, which shows a high accuracy in distinguishing a
disease from another.
transmission versus distribution planning, long term versus short term planning,issues in transmission planning,generation planning,capacity resource planning, transmission planning,national and regional planning, integrated resource planning
SAIEE presentation - Power System Resilience - Why should we CARE as energy u...Malcolm Van Harte
Presentation on the latest Power System Resilience thinking:
Thanks to the SAIEE load research chapter for the opportunity to share the interesting work from CIGRE C4.47 WG - Power System Resilience Working Group.
Key questions addressed:
1. What is Power System Resilience?
2. What are the principles?
3. Why is it important to network planners?
4. How does one go about it?
View WEBINAR on Dropbox link here:
https://www.dropbox.com/s/j87z1ljmj55uddx/20200421%20Network%20resilience%20M%20V%20Harte.mp4?dl=0
Design and Analysis of PID and Fuzzy-PID Controller for Voltage Control of DC...Francisco Gonzalez-Longatt
DC microgrids are desired to provide the electricity for the remote areas which are far from the main grid. The microgrid creates the open horizontal environment to interconnect the distributed generation especially photovoltaic (PV). The stochastic nature of the PV output power introduces the large fluctuations of the power and voltage in the microgrid and forced to introduce the controller for voltage stability. There are many control strategies to control the voltage of a DC microgrid in the literature. In this paper the proportional-integral-derivative (PID) and fuzzy logic PID (FL-PID) controller has been designed and compared in term of performance. Performance measures like maximum overshoot and settling time of FL-PID compared with the PID proved that the former is better controller. The controllers are designed and simulated in the MATLAB programming environment. The controllers has been tested for the real time data obtained from Pecan Street Project, University of Texas at Austin USA.
Low-cost convolutional neural network for tomato plant diseases classificationIAESIJAI
Agriculture is a crucial element to build a strong economy, not only because
of its importance in providing food, but also as a source of raw materials for
industry as well as source of energy. Different diseases affect plants, which
leads to decrease in productivity. In recent years, developments in
computing technology and machine-learning algorithms (such as deep neural
networks) in the field of agriculture have played a great role to face this
problem by building early detection tools. In this paper, we propose an
automatic plant disease classification based on a low complexity
convolutional neural network (CNN) architecture, which leads to faster online classification. For the training process, we used more than one 57,000
tomato leaf images representing nine classes, taken under natural
environment, and considered during training without background
subtraction. The designed model achieves 97.04% classification accuracy
and less than 0.2 error, which shows a high accuracy in distinguishing a
disease from another.
Hierarchical Droop Controlled Frequency Optimization and Energy Management of a Grid-Connected Microgrid ,
Sustech 2017 conference, Nov 12-14
Presented by Sima Aznavi
Avionics 738 Adaptive Filtering at Air University PAC Campus by Dr. Bilal A. Siddiqui in Spring 2018. This lecture covers background material for the course.
The myphotonics project deals with the construction of opto-mechanical components and optical experiment implementation using modular systems such as LEGO®.
The components are low cost and the instructions that originated them are free to use. OpenAdaptonik and myphotonics can work together sharing the same purpose.
Some approximation properties of modified baskakov stancu operatorseSAT Journals
Abstract In the present paper, we prove a global direct theorem for the modified Baskakovstancu operators in terms of Ditzian-Totik modulus of smoothness. Here, we have modified our operators by taking weight function of Beta operators and then generalizing it as stancu type generalized operators. We will also see that taking weight function of Beta operators will give better approximation. We study a global direct theorem using simultaneous approximation for ourstancu type generalized operator in 퐿푝 0,∞ . Here, first we estimate recurrence relation for moments and then develop some global direct results by making our stancu type generalized operators positive using differential and integral operators. In this paper, our effort is to give better global approximation for our stancu type generalized operator than the earlier integral modifications of Baskakov operators studied by various authors. Here, we will extend our results for the whole interval 0,∞ . In this paper, we will also make use of the fact that second modulus of smoothness introduced by Ditzian-Totik is equivalent to modified k-functional and 퐿푝푟[0,∞) is not contained in 퐿1푟[0,∞) for obtaining results. Here, Riesz-Thorin theorem and Leibnitz theorem is used extensively for doing simultaneous approximation. We have also used Fubini’s theorem for obtaining results. Key words: Stancu type generalization, simultaneous approximation, modulus of smoothness.
Impact of Electrification on Asset Life Degradation and Mitigation with DERPower System Operation
Distribution networks are currently faced with a plethora of changes in resources, equipment technology, structure, and loading. First, Distributed Energy Resources (DERs) have been increasingly penetrating distribution grids worldwide. DERs have been recognized as a Non-Wires Alternative (NWA) in certain use cases including peak shaving, renewable integration etc). The second imminent change in distribution networks is the electrification of loads, especially in the transportation and space heating sectors, driven at least in part by clean-air and sustainability goals. Electrification is expected to result in higher peak load levels as well as flatter daily and annual load shapes, due to the fact that it is primarily composed of off-peak and by storage-like loads like those of EVs, storage, and electric heating. Their valley-filling behavior results in distribution network apparatus being consistently loaded to high utilization levels.
As a result of these changes in load curve shape, distribution equipment may be subjected to increased operational stress compared to what it endured in the past, even if not loaded to higher net peak loads. For example, in the United States, the majority of distribution substation transformers typically warm up during the morning and afternoon as they approach demand peaks and then cool down afterwards as loading falls. Cumulative loss of life from this repetitive daily cycle is slow, so that expected service life of a typical unit is on the order of fifty years or more, even allowing for periods of intense overload during very rare contingencies. This has been the norm for the US electric utility industry in the last seventy years, but may no longer be the case in environments where electrification is more prevalent.
Multi-Objective Aspects of Distribution Network Volt-VAr OptimizationPower System Operation
Recent research has enabled the integration of traditional Volt-VAr Control (VVC) resources, such as capacitors banks and transformer tap changers, with Distributed Energy Resources (DERs), such as photovoltaic farms and batteries, in order to achieve various Volt-VAr Optimization (VVO) targets, such as Conservation Voltage Reduction (CVR), minimizing VAr flow at the transformer, minimizing grid losses, minimizing asset operations and more. In this case, where more than one target function is involved, the question of multi-objective optimization is raised. In this work, we demonstrate various methods in which such optimization can be performed in practice and we discuss the various operational considerations that are involved with each method. We demonstrate the methods using simulation on a test feeder
On the dynamic behavior of the current in the condenser of a boost converter ...TELKOMNIKA JOURNAL
In this paper, an analytical and numerical study is conducted on the dynamics of the current in the condenser of a boost converter controlled with ZAD, using a pulse PWM to the symmetric center. A stability analysis of periodic 1T-orbits was made by the analytical calculation of the eigenvalues of the Jacobian matrix of the dynamic system, where the presence of flip and Neimar–Sacker-type bifurcations was determined. The presence of chaos, which is controlled by ZAD and FPIC techniques, is shown from the analysis of Lyapunov exponents.
Sustainable Energy Consumption in Public Buildings: The Significance of Occu...encompassH2020
This paper investigates what determinants of sustainable energy consumption in public buildings are most studied and tests the impact of some energy saving determinants on employees’ behaviour in public buildings in Greece.
Visualizing and gamifying consumption data for resource savingencompassH2020
Visualizing and gamifying consumption data for resource saving: challenges, lessons learnt and a research agenda for the future.
enCOMPASS presentation at the 8th DACH+ Conference on Energy Informatics, Salzburg, Austria, September 2019
enCOMPASS poster: Less Energy, smarter living!encompassH2020
A poster explaining the main goals and activities of the enCOMPASS project.
The enCOMPASS project aims at developing innovative user-friendly digital tools
for making energy consumption data available and understandable to
everyone. This will empower and involve people, so that they work
together to save energy and directly manage their energy
needs. In turn, this will maximise energy efficiency,
bringing down costs while still preserving comfort.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
3. Metering equipment
Landis+Gyr smart meter
Clemap sub metering device
Smart meters
Sub-metering devices
Low frequency: 1Hz
High frequency: 1.2 - 2 Khz
Ultra low frequency: 0.001 Hz
(1 sample every 15 mins)
4. Datasets
• Various public datasets have been collected over the
years, such as:
• REDD: 6 homes, 15Khz, 119 days
• UK-DALE: 5 homes, 16Khz, 655 days
• BLUED: one home, 12Khz, 1 week
• GREEND: 8 homes, 1Hz, 1 year
• AMPds: 1 house, 1Hz, 2 years,
5. The recorded data
• Information about the
household (type, inhabitants,
etc)
• Aggregate power at given
resolution (sometimes active
and apparent power)
• Power consumption of each
appliance
(Kelly and Knottenbelt, 2015)
6. Applications
• Identify energy inefficient devices
• Provide feedback on energy usage
• Stimulate energy saving behaviours
• Understand and extrapolate user behaviour
9. Algorithm assumptions
• We need an algorithm able to deal with ultra low frequency
data
• In a previous work it was assumed that the power demand
profiles were piecewise constant
• We treat the problem as a least-square minimisation with a
penalty term to impose the piecewise-constant shape
• This is difficult to hold for ultra-low frequencies
• The problem has been formulated as a mixed integer QP
10. The objective function
the set of appliances
set of energy consumption levels of appliance a
1 if appliance a can be turned on at time t
1 if appliance a operates at consumption level l during time epoch t
the aggregate energy consumption during time epoch t
is the multiplicative weight of appliance a
1 if appliance a changes consumption level at time epoch t
𝑚𝑖𝑛 ∑
𝑡∈𝑇
𝑐𝑡 − ∑
𝑎∈𝐴,𝑙∈𝐿 𝑎
𝑙 ⋅ 𝑢 𝑎,𝑡 ⋅ 𝑥 𝑎,𝑙,𝑡
2
+ ∑
𝑡∈𝑇,𝑎∈𝐴
𝛼 𝑎 ⋅ 𝑦 𝑎,𝑡
Fit to consumption Penalize on/off switching
11. The objective function
the set of appliances
set of energy consumption levels of appliance a
1 if appliance a can be turned on at time t
1 if appliance a operates at consumption level l during time epoch t
the aggregate energy consumption during time epoch t
the multiplicative weight of appliance a
1 if appliance a changes consumption level at time epoch t
𝑚𝑖𝑛 ∑
𝑡∈𝑇
𝑐𝑡 − ∑
𝑎∈𝐴,𝑙∈𝐿 𝑎
𝑙 ⋅ 𝑢 𝑎,𝑡 ⋅ 𝑥 𝑎,𝑙,𝑡
2
+ ∑
𝑡∈𝑇,𝑎∈𝐴
𝛼 𝑎 ⋅ 𝑦 𝑎,𝑡
12. The objective function
the set of appliances
set of energy consumption levels of appliance a
1 if appliance a can be turned on at time t
1 if appliance a operates at consumption level l during time epoch t
the aggregate energy consumption during time epoch t
the multiplicative weight of appliance a
1 if appliance a changes consumption level at time epoch t
𝑚𝑖𝑛 ∑
𝑡∈𝑇
𝑐𝑡 − ∑
𝑎∈𝐴,𝑙∈𝐿 𝑎
𝑙 ⋅ 𝑢 𝑎,𝑡 ⋅ 𝑥 𝑎,𝑙,𝑡
2
+ ∑
𝑡∈𝑇,𝑎∈𝐴
𝛼 𝑎 ⋅ 𝑦 𝑎,𝑡
13. The objective function
the set of appliances
set of energy consumption levels of appliance a
1 if appliance a can be turned on at time t
1 if appliance a operates at consumption level l during time epoch t
the aggregate energy consumption during time epoch t
the multiplicative weight of appliance a
1 if appliance a changes consumption level at time epoch t
𝑚𝑖𝑛 ∑
𝑡∈𝑇
𝑐𝑡 − ∑
𝑎∈𝐴,𝑙∈𝐿 𝑎
𝑙 ⋅ 𝑢 𝑎,𝑡 ⋅ 𝑥 𝑎,𝑙,𝑡
2
+ ∑
𝑡∈𝑇,𝑎∈𝐴
𝛼 𝑎 ⋅ 𝑦 𝑎,𝑡
14. The objective function
the set of appliances
set of energy consumption levels of appliance a
1 if appliance a operates (at consumption level l) during time epoch t
the aggregate energy consumption during time epoch t
the multiplicative weight of appliance a
1 if appliance a changes consumption level at time epoch t
𝑚𝑖𝑛 ∑
𝑡∈𝑇
𝑐𝑡 − ∑
𝑎∈𝐴,𝑙∈𝐿 𝑎
𝑙 ⋅ 𝑢 𝑎,𝑡 ⋅ 𝑥 𝑎,𝑙,𝑡
2
+ ∑
𝑡∈𝑇,𝑎∈𝐴
𝛼 𝑎 ⋅ 𝑦 𝑎,𝑡
1 if appliance a can be turned on at time t
15. The objective function
the set of appliances
set of energy consumption levels of appliance a
1 if appliance a operates at consumption level l during time epoch t
the aggregate energy consumption during time epoch t
is the multiplicative weight of appliance a
1 if appliance a changes consumption level at time epoch t
𝑚𝑖𝑛 ∑
𝑡∈𝑇
𝑐𝑡 − ∑
𝑎∈𝐴,𝑙∈𝐿 𝑎
𝑙 ⋅ 𝑢 𝑎,𝑡 ⋅ 𝑥 𝑎,𝑙,𝑡
2
+ ∑
𝑡∈𝑇,𝑎∈𝐴
𝛼 𝑎 ⋅ 𝑦 𝑎,𝑡
1 if appliance a can be turned on at time t
16. The objective function
𝑚𝑖𝑛 ∑
𝑡∈𝑇
𝑐𝑡 − ∑
𝑎∈𝐴,𝑙∈𝐿 𝑎
𝑙 ⋅ 𝑢 𝑎,𝑡 ⋅ 𝑥 𝑎,𝑙,𝑡
2
+ ∑
𝑡∈𝑇,𝑎∈𝐴
𝛼 𝑎 ⋅ 𝑦 𝑎,𝑡
the set of appliances
set of energy consumption levels of appliance a
1 if appliance a can be turned on at time t
1 if appliance a operates at consumption level l during time epoch t
the aggregate energy consumption during time epoch t
the multiplicative weight of appliance a
1 if appliance a changes consumption level at time epoch t
22. Constraints
∀𝑎 ∈ 𝐴∑
𝑡∈𝑇
𝑜 𝑎,𝑡 ≥ 𝑤 𝑎 ⋅ 𝑓𝑎
𝑜 𝑎,𝑡 ⋅ 𝑡 − 𝑜 𝑎,𝑡′ ⋅ (𝑡′) ≤ 𝑑 𝑎 1 − |𝑇| ⋅ (𝑜 𝑎,𝑡 + 𝑜 𝑎,𝑡′ − 2)
∀𝑎 ∈ 𝐴; 𝑡, 𝑡′ ∈ 𝑇2: 𝑡 > 𝑡′
∑
𝑙∈𝐿 𝑎
𝑙 ⋅ 𝑢 𝑎,𝑡 ⋅ 𝑥 𝑎,𝑙,𝑡 ≤ 𝑚𝑎𝑥
𝑙∈𝐿 𝑎
𝑙 ⋅ 𝑜 𝑎,𝑡
the daily energy consumption of appliance a (if
activated) is not lower than the daily lower limit w
[f is 1 if a is on at least once over 0..T]
∀𝑡 ∈ 𝑇, 𝑎 ∈ 𝐴
23. Constraints
𝑓𝑎 ⋅ |𝑇| ≥ ∑
𝑙∈𝐿 𝑎,𝑡∈𝑇
𝑙 ⋅ 𝑢 𝑎,𝑡 ⋅ 𝑥 𝑎,𝑙,𝑡 ∀𝑎 ∈ 𝐴
Makes sure that f and x are coherent
∑
𝑡∈𝑇
𝑢 𝑎,𝑡 ⋅ 𝑥 𝑎,𝑙′,𝑡 ≥ 𝑓𝑎 ∀𝑎 ∈ 𝐴
˜
, 𝑙′ = 𝑚𝑎𝑥
𝑙∈𝐿 𝑎
𝑙
24. Constraints
𝑓𝑎 ⋅ |𝑇| ≥ ∑
𝑙∈𝐿 𝑎,𝑡∈𝑇
𝑙 ⋅ 𝑢 𝑎,𝑡 ⋅ 𝑥 𝑎,𝑙,𝑡 ∀𝑎 ∈ 𝐴
Each appliance in set A works at maximum at the highest
consumption level if activated
∑
𝑡∈𝑇
𝑢 𝑎,𝑡 ⋅ 𝑥 𝑎,𝑙′,𝑡 ≥ 𝑓𝑎
∀𝑎 ∈ 𝐴
˜
, 𝑙′
= 𝑚𝑎𝑥
𝑙∈𝐿 𝑎
𝑙
Note: our set includes the dishwasher, the tumbler, the
washing machine
25. Constraints
𝑤𝑚 ≥ 𝑜 𝑤𝑚,𝑡 ⋅ 𝑡
∀𝑡 ∈ 𝑇𝑐𝑑 ≤ 𝑜 𝑐𝑑,𝑡 ⋅ 𝑡 + |𝑇| ⋅ (1 − 𝑜 𝑐𝑑,𝑡)
∀𝑡 ∈ 𝑇𝑐𝑑 ≥ 𝑤𝑚 + 1
wm is set to the last time epoch during which the washing
machine is active
26. Constraints
𝑤𝑚 ≥ 𝑜 𝑤𝑚,𝑡 ⋅ 𝑡
∀𝑡 ∈ 𝑇𝑐𝑑 ≤ 𝑜 𝑐𝑑,𝑡 ⋅ 𝑡 + |𝑇| ⋅ (1 − 𝑜 𝑐𝑑,𝑡)
∀𝑡 ∈ 𝑇𝑐𝑑 ≥ 𝑤𝑚 + 1
sets variable cd to the
first epoch of activity of the clothes dryer
27. Constraints
𝑤𝑚 ≥ 𝑜 𝑤𝑚,𝑡 ⋅ 𝑡
∀𝑡 ∈ 𝑇𝑐𝑑 ≤ 𝑜 𝑐𝑑,𝑡 ⋅ 𝑡 + |𝑇| ⋅ (1 − 𝑜 𝑐𝑑,𝑡)
∀𝑡 ∈ 𝑇𝑐𝑑 ≥ 𝑤𝑚 + 1
imposes that the clothes dryer is turned on after the end of
the operational period of the washing machine
28. Constraints
∑
𝑎∈𝐴,𝑙∈𝐿 𝑎,𝑡∈𝑇
𝑙 ⋅ 𝑢 𝑎,𝑡 ⋅ 𝑥 𝑎,𝑙,𝑡 ≤ ∑
𝑡∈𝑇
𝑐𝑡
imposes that the sum of the disaggregated energy
consumption profiles does not exceed the total energy
usage measured by the smart meter located at the user’s
premises.
29. Solving the model
• The horizon length T is selected
• The set A is defined
• The set of consumption levels L_a are defined (extracted from training
data)
• Parameter c is extracted from measured aggregated power
• Parameters m,d,w are either learned from data or from public datasets
• Parameter u is used to prevent switching on appliances at some epochs
• Parameter alpha is used to modulate the assumption of piecewise
constantness
30. Evaluation
• Training and validation performed using UK-DALE dataset
• Used 3 buildings:
• building 1 from April 1, 2013 to May 31, 2013,
• building 2 from May 1, 2013 to June 30, 2013,
• building 5 from July 1, 2014 to August 31, 2014.
• In the numerical assessment, we considered a scenario where
performed the disaggregation of the 5 top consuming appliances,
• Comparison with HMM and CO from NILMTK (Batra 2014)
38. The enCOMPASS case
User 79
{"date_start":"2019-08-12",
"date_end":"2019-09-10",
"fridge":67.2,
"washing_machine":23.75,
“tumble_dryer":18.3,
“dishwasher":38.8,
“electric_oven":31.2,
“other":72.499,
"total_consumption":251.749}
39. Conclusions
• The proposed algorithm compares to the state-of-the-art
algorithms when applied to low frequency data
• It has the nice property of performance degrading
smoothly with the decrease of the sampling frequency
• The disaggregation at 15 minutes resolution can provide
only an aggregate (daily, weekly) indication of how energy
has been shared across appliances
40. Acknowledgments
• This research received funding from the enCOMPASS
project (Grant N. 723059)
• http://www.encompass-project.eu