From Load Forecasting to Demand Response - A Web of Things Use Case

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This paper provides a Web of Things use case from a personalized load forecasting service to a gamied demand response program. Combining real-world measuring applications with web-based applications opens new opportunities to the smart grid. For this purpose, we propose a Web of Things framework for a novel load forecasting process at the appliance level. Firstly, we illustrate the concept design of the Web of Things framework consisting of the sensing infrastructure, the activity recognition and the load forecasting modules. Secondly, we show how we guarantee the modularity and flexibility for implementing all the three modules in a web- based manner. On top of our infrastructure, we propose an extended Web of Things use case by integrating our load forecasting approach into a demand response concept.

Science

Technology for
Pervasive Computing
From Load Forecasting to Demand Response
- A Web of Things Use Case
The 5th International Workshop on the Web of Things (WoT 2014)
Yong Ding, Martin A. Neumann, Till Riedel, Michael Beigl, TECO, KIT, Germany
Ömer Kehri, CAS Software AG, Germany
Geoff Ryder, SAP Palo Alto, USA
KIT – University of the State of Baden-Wuerttemberg and
National Research Center of the Helmholtz Association www.kit.edu

C2G: customer as active participant of a Smart Grid
Goal: For more predictable and managed demand
HUMANS THINGS
WEB
Load forecasting based on human activity and
context analysis in a web-based manner.
Technology for
Pervasive Computing
2 16.10.2014
Yong Ding et al. @ WoT 2014
AI

Technology for
Pervasive Computing
The Context: Load & Activity Duality
3 16.10.2014
Urban Area
City Blocks
Houses
Flats
Devices
Yong Ding et al. @ WoT 2014
Urban Area
Social Group
Person
Energy
Measurement
Behavioural
Recognition
Ground Truth Model Input

We shouldn‘t we be able to generate other
business models in the Web?
Technology for
Pervasive Computing
Dynamic Pricing for Households
Residents react to a price signal
Money is very generic
A lot of information
That‘s no fun: Long-term interest?
4 16.10.2014
Yong Ding et al.

links the mobile games world with the energy system
Technology for
Pervasive Computing
The “Bet and Energy” Idea
5 16.10.2014
Yong Ding et al. @ WoT 2014

Residents bet on energy consumption of
devices and entire households
Rich interaction: Use analytics, improve your
skills
Long-term interest!
(given fair chance of winning)
Residents can win discounts on their bill and
other prizes
Technology for
Pervasive Computing
Goal: Increase predictability of residential
demand
6 16.10.2014
Yong Ding et al.

Technology for
Pervasive Computing
7 16.10.2014 Yong Ding et al.

Technology for
Pervasive Computing
WoT Forecasting Framework – I
8 16.10.2014
Yong Ding et al. @ WoT 2014

Technology for
Pervasive Computing
WoT Forecasting Framework – II
Main features
Data collection via REST API
Domain specific modules
9 16.10.2014
Resource Oriented Namespaces
based on Metadata (Id, Type,…)
Push-notfiication based on simple
push scheme (Long Poll, SSE)
Modular persistence support for
historical data (OpenTSDB, SQLite)
activity recognition
load forecasting
Guarantee of modularity & flexibility
Yong Ding et al. @ WoT 2014

Technology for
Pervasive Computing
Integration of Bet and Energy
10 16.10.2014
Yong Ding et al. @ WoT 2014

Smart Meters Bet Acceptance
Utility uses databases to offer bets on regional
marketplaces
Technology for
Pervasive Computing
Bet and Energy Design
Multilayered web architecture
Smart Meter & Mobile App
Meters report to regional databases
Residents use mobile apps to
Monitor consumption
Close bets
Redeem prizes
11 16.10.2014
Yong Ding et al. @ WoT 2014
Bet Offering
Utility
Resident

Summary
Activity recognition module
Load forecasting module
Modular and flexible design for real-time execution and
evaluation
Technology for
Pervasive Computing
WoT based forecasting infrastructure
Use Case: Bet and Energy web app
As a first proof-of-concept application
12 16.10.2014
Yong Ding et al. @ WoT 2014

Technology for
Pervasive Computing
Urban Area
City Blocks
Houses
Flats
Devices
13 16.10.2014 Yong Ding et al.
Urban Area
Social Group
Person
Energy
Measurement
Behavioural
Recognition
Ground Truth Model Input

Technology for
Pervasive Computing
That’s All
14 16.10.2014
Thank You!
Questions?
Yong Ding et al. @ WoT 2014

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Context. WASP-76 b has been a recurrent subject of study since the detection of a signature in high-resolution transit spectroscopy data indicating an asymmetry between the two limbs of the planet. The existence of this asymmetric signature has been confirmed by multiple studies, but its physical origin is still under debate. In addition, it contrasts with the absence of asymmetry reported in the infrared (IR) phase curve. Aims. We provide a more comprehensive dataset of WASP-76 b with the goal of drawing a complete view of the physical processes at work in this atmosphere. In particular, we attempt to reconcile visible high-resolution transit spectroscopy data and IR broadband phase curves. Methods. We gathered 3 phase curves, 20 occultations, and 6 transits for WASP-76 b in the visible with the CHEOPS space telescope. We also report the analysis of three unpublished sectors observed by the TESS space telescope (also in the visible), which represents 34 phase curves. Results. WASP-76 b displays an occultation of 260±11 and 152±10 ppm in TESS and CHEOPS bandpasses respectively. Depending on the composition assumed for the atmosphere and the data reduction used for the IR data, we derived geometric albedo estimates that range from 0.05 ± 0.023 to 0.146 ± 0.013 and from <0.13 to 0.189 ± 0.017 in the CHEOPS and TESS bandpasses, respectively. As expected from the IR phase curves, a low-order model of the phase curves does not yield any detectable asymmetry in the visible either. However, an empirical model allowing for sharper phase curve variations offers a hint of a flux excess before the occultation, with an amplitude of ∼40 ppm, an orbital offset of ∼−30◦ , and a width of ∼20◦ . We also constrained the orbital eccentricity of WASP-76 b to a value lower than 0.0067, with a 99.7% confidence level. This result contradicts earlier proposed scenarios aimed at explaining the asymmetry observed in high-resolution transit spectroscopy. Conclusions. In light of these findings, we hypothesise that WASP-76 b could have night-side clouds that extend predominantly towards its eastern limb. At this limb, the clouds would be associated with spherical droplets or spherically shaped aerosols of an unknown species, which would be responsible for a glory effect in the visible phase curves.

Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b

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Cotton crops are vulnerable to a variety of sucking pests, which can severely impact plant health, yield, and fiber quality. These pests primarily feed on plant sap, extracting nutrients directly from the plant's vascular system. Here's a breakdown of some of the most significant sucking pests in cotton cultivation: Aphids: Cotton aphids or melon aphids can cause direct damage by sucking sap and indirect damage by secreting honeydew, which encourages sooty mold growth. This can interfere with photosynthesis and weaken the plant. Aphids can also transmit viral diseases. Whiteflies: Two species, the silverleaf whitefly and the bandedwinged whitefly, are particularly troublesome. They not only suck sap from the underside of leaves, causing yellowing and leaf drop, but their honeydew excretion promotes sooty mold and they can transmit several plant viruses. Thrips: While thrips can chew on plants, their primary damage to cotton is through sucking. They attack the cotton plant during its seedling stage, which can stunt growth and reduce vigor. Thrips are also capable of transmitting the Cotton Bud disease. Spider Mites: These are not insects but arachnids. Spider mites, such as the two-spotted spider mite, suck cell contents from the leaves, leading to speckled discoloration and potentially significant leaf loss if infestations are severe. Leafhoppers: Including various species, leafhoppers can cause direct damage through feeding, which results in leaf curling and stunted growth. They can also be vectors for plant diseases. Mealybugs: These pests are less common but can be problematic, especially in clustered planting conditions. They suck sap and secrete honeydew, which leads to sooty mold. Mealybugs can also spread viruses. Stink Bugs: Although primarily known for their chewing mouthparts, certain stink bugs can cause damage similar to sucking pests by injecting saliva into the plant and sucking out nutrients, leading to boll damage and stained lint. Management Strategies: Cultural Controls: This includes practices such as crop rotation, using resistant varieties, and managing planting and harvesting times to avoid peak pest populations. Biological Controls: Beneficial insects like lady beetles, lacewings, and predatory mites can naturally control sucking pest populations. Parasitic wasps also play a role in controlling aphid and whitefly populations. Chemical Controls: Insecticides may be used but should be chosen carefully to minimize resistance development and preserve beneficial insects. Systemic insecticides can be particularly effective against sucking pests. Integrated Pest Management (IPM): Combining multiple control strategies based on monitoring and thresholds to apply the most effective and environmentally sensitive approach. Effective management of sucking pests in cotton requires a thorough understanding of the pest species present, their life cycles, and the ecological balance of the field environment.

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Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune Waterworlds

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From Load Forecasting to Demand Response - A Web of Things Use Case

1. Technology for Pervasive Computing From Load Forecasting to Demand Response - A Web of Things Use Case The 5th International Workshop on the Web of Things (WoT 2014) Yong Ding, Martin A. Neumann, Till Riedel, Michael Beigl, TECO, KIT, Germany Ömer Kehri, CAS Software AG, Germany Geoff Ryder, SAP Palo Alto, USA KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association www.kit.edu

2. C2G: customer as active participant of a Smart Grid Goal: For more predictable and managed demand HUMANS THINGS WEB Load forecasting based on human activity and context analysis in a web-based manner. Technology for Pervasive Computing 2 16.10.2014 Yong Ding et al. @ WoT 2014 AI

3. Technology for Pervasive Computing The Context: Load & Activity Duality 3 16.10.2014 Urban Area City Blocks Houses Flats Devices Yong Ding et al. @ WoT 2014 Urban Area Social Group Person Energy Measurement Behavioural Recognition Ground Truth Model Input

4. We shouldn‘t we be able to generate other business models in the Web? Technology for Pervasive Computing Dynamic Pricing for Households Residents react to a price signal Money is very generic A lot of information That‘s no fun: Long-term interest? 4 16.10.2014 Yong Ding et al.

5. links the mobile games world with the energy system Technology for Pervasive Computing The “Bet and Energy” Idea 5 16.10.2014 Yong Ding et al. @ WoT 2014

6. Residents bet on energy consumption of devices and entire households Rich interaction: Use analytics, improve your skills Long-term interest! (given fair chance of winning) Residents can win discounts on their bill and other prizes Technology for Pervasive Computing Goal: Increase predictability of residential demand 6 16.10.2014 Yong Ding et al.

7. Technology for Pervasive Computing 7 16.10.2014 Yong Ding et al.

8. Technology for Pervasive Computing WoT Forecasting Framework – I 8 16.10.2014 Yong Ding et al. @ WoT 2014

9. Technology for Pervasive Computing WoT Forecasting Framework – II Main features Data collection via REST API Domain specific modules 9 16.10.2014 Resource Oriented Namespaces based on Metadata (Id, Type,…) Push-notfiication based on simple push scheme (Long Poll, SSE) Modular persistence support for historical data (OpenTSDB, SQLite) activity recognition load forecasting Guarantee of modularity & flexibility Yong Ding et al. @ WoT 2014

10. Technology for Pervasive Computing Integration of Bet and Energy 10 16.10.2014 Yong Ding et al. @ WoT 2014

11. Smart Meters Bet Acceptance Utility uses databases to offer bets on regional marketplaces Technology for Pervasive Computing Bet and Energy Design Multilayered web architecture Smart Meter & Mobile App Meters report to regional databases Residents use mobile apps to Monitor consumption Close bets Redeem prizes 11 16.10.2014 Yong Ding et al. @ WoT 2014 Bet Offering Utility Resident

12. Summary Activity recognition module Load forecasting module Modular and flexible design for real-time execution and evaluation Technology for Pervasive Computing WoT based forecasting infrastructure Use Case: Bet and Energy web app As a first proof-of-concept application 12 16.10.2014 Yong Ding et al. @ WoT 2014

13. Technology for Pervasive Computing Urban Area City Blocks Houses Flats Devices 13 16.10.2014 Yong Ding et al. Urban Area Social Group Person Energy Measurement Behavioural Recognition Ground Truth Model Input

14. Technology for Pervasive Computing That’s All 14 16.10.2014 Thank You! Questions? Yong Ding et al. @ WoT 2014

Editor's Notes

Savings in generation, distribution, storage
Multilayered web architecture consisting of: Relational database: SQL e.g. MySQL Back end: Java Database abstraction e.g. JPA2/EBean RESTful Web Services e.g. Jersey (JAX-RS) auf Grizzly Front end: HTML/JavaScript (Single-Page) e.g. AngularJS/Backbone.js, Bootstrap, jQuery, ...

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