Team Messaging

This week I added messaging feature to FigureEnergy. Hereafter team members are able to contact each other through the new feature.

I also read a bunch of academic papers:

- SmartCap: Flattening Peak Electricity Demand in Smart Homes

- Agent-Based Control for Decentralised Demand Side Management in the Smart Grid

- Multi-Agent Control of Thermal Systems in Buildings

- Smart Heat Grid on a Intraday Power Market

- An Agent Framework for Knowledge-Based Homes

- Improving Building Energy Efficiency with a Network of Sensing, Learning and Prediction Agents

- Intelligent Heating Systems in Households for Smart Grid Applications

- Two Theories of Home Heat Control

- An Intelligent Agent for Home Heating Management

- A Scalable Low-Cost Solution to Provide Personalized Home Heating Advice to Households

I will update this post with providing short descriptions of the papers. Meanwhile, I am reading  papers related to energy from top conferences and journals. I also started to read the book 'Pattern Recognition and Machine Learning' by Bishop.

Human-Agent Interaction

This post will summarise  TeamCore research group's studies which are related to energy efficiency and human-agent interaction. http://teamcore.usc.edu/junyounk/energy/

- Human-Building Interaction for Energy Conservation in Office Building

The study aims to increase energy awareness through a reminder system which delivers simple and complex messages to occupants in order to change their energy consumption behaviour. While simple messages only involve suggestions about how occupants can behave to decrease energy consumption, complex messages additionally include information about the consequences of the suggested behaviours. The point emphasized in the study is that intensive feedbacks that contain the outcomes of the occupants' behaviour are more powerful than simple suggestions to convince people to adopt green habits.

 

- Towards Robust Multi-Objective Optimization Under Model Uncertainty for Energy Conservation

The aim of the work is to maximise energy savings without affecting the comfort level of occupants in commercial buildings. There are two types of agents which are room agents and proxy agents. Proxy agents represent occupants through containing occupant's model, and they interact with Room agents on behalf of occupants. Room agents represent the offices or conference rooms, and their task is to reduce energy consumption in the represented room through negotiating with Proxy agents to minimise the use of office devices (lights, HVAC, etc.) or relocating meetings to more efficient smaller rooms. An interesting point is that irritation levels are examined in the study (irritation might be occur when an occupant gets a message by her Proxy agent frequently). According to examination, a simple message might reach much higher irritation levels than a complex message when the frequency of message sending is increased.

- Automation in Construction

Multi-agent comfort and energy system (MACES) provides an alternative model to manage and control building devices and occupants considering actual thermal zones, temperatures, occupant preferences, and occupant schedules. There are three types of agents which are device agents, human agents and meeting agents. Device agents represent HVAC, lighting and appliance agents. They mainly monitor and control sensors and switches. Human agents represent permanent and temporary occupants who possess different behaviours, schedules and preferences. The simulation of the system utilise four control strategies which are baseline, reactive, proactive and proactive-MDP. The control capabilities varies for each strategy from just adjusting temperature and lighting to relocating meetings. It is stated that proactive-MDP strategy in which agents additionally can change meeting schedules outperforms other strategies in terms of energy savings and comfort levels.

- A Multi-Sensor Based Occupancy Estimation Model for Supporting Demand Driven HVAC Operations

This paper focuses on the estimating the occupancy in buildings so as to adjust HVAC operations. The estimation model is built on several sensors. Each sensor node includes a light sensor, a sound sensor, a motion sensor, a humidity sensor, a CO₂ sensor, a temperature sensor and a PIR sensor. To estimate the number of occupants, sensor data is processed through radial basis function network. The overall detection rate is 87.62% for self-estimation and 64.83% for cross-estimation. The sensor node costs about $230 USD.

The following papers basically repeat the work explained in the above. However, they provide some brief descriptions of calculations utilised in the simulation.

- SAVES: A Sustainable Multi-Agent Application to Conserve Building Energy Considering Occupants

- Towards Optimal Planning for Distributed Coordination Under Uncertainty in Energy Domains

 

 

 

 

Heating Up

I read the following articles:

- A Negotiation Protocol for Multiple Interdependent Issues Negotiation Over Energy Exchange

This paper proposes a negotiation protocol in which off-grid households assumed to have renewable energy generation and electricity storage facilities exchange energy. Agents may benefit more through exchanging energy as they will reduce their storage losses. I liked the idea of the creation of a grid which is formed by off-grid households that generates their own energy and exchange the energy among themselves.

- Practical Distributed Coalition Formation via Heuristic Negotiation in Social Networks

The paper provides a novel framework for decentralised coalition formation in social networks. In the framework, agents can form coalitions without knowing peer's preferences through negotiations.  It is also demonstrated that the new negotiation strategy can increase social welfare by up to 10%.

- A Scoring Rule-Based Mechanism for Aggregate Demand Prediction in the Smart Grid

The mechanism called sum of others' plus(SOM) is developed to fairly distribute the savings obtained by agents. Unlike uniform mechanism which divides the savings equally among agents, SOM divides the savings according to each agent's contribution. This encourages agents to produce more accurate and costly estimates of future events.

- A panel model for predicting the diversity of internal temperatures from English dwellings

 

This work utilises panel methods to create a model which predicts internal temperature of households with more accuracy. The model considers both building stocks and human behaviour to predict internal temperature demand.  The number of occupants, household income, occupant age, building typology, building age, roof insulation thickness and wall U-value are some of the factors included in the model. The model can predict internal temperature of different building stocks within ~0.71°C at 95% confidence and explain 45% of the change of internal temperature among households.

 

- Use and usability of central heating controls

 

In this paper, the usability of heating controls is assessed through an experiment in which participants are asked to perform some tasks which represent the functionalities provided by the control. The result of the experiment states that heating controls are not well-designed enough to clearly express its all functionalities as the participants had difficulties to perform the most of the tasks.

 

 

 

Norman Thing

I finished reading the book "The Design of Everyday Things" by Norman, so I wanted to write some short notes to remind me later what I found interesting in the book. These notes do not cover everything mentioned in the book and I might miss some points since I only read it once. However, I can say that reading this book changed the way I see the world, which is strange. Although we live same things everyday, we generally cannot recognise them by ourselves unless we read or hear them from external sources. 

1- It is not our fault! The design has defect.

2- Conceptual models, feedback, constraints, affordance are the principles that a designer should focus on.

3- The Seven Stages of Action:

Forming Goal -> Forming the intention -> Specifying an action -> Executing the action ->
Perceiving the state of the world -> Interpreting the state of the world -> Evaluating the outcome

The greens are for execution while the yellows are for evaluation.

4-  What should be done for a good design?

• Utilise both knowledge in the world and in the head.
• Simplify the structure of tasks (balance depth and width).
• Make things visible: bridge the gap between execution and evaluation.
• Get the mappings right.
• Use the power of constraints (natural and artificial).
• Always consider errors and design for them.
• When all else fails, standardise (arbitrary).

Now, I started to read the book "Plans and Situated Actions" by Suchman.