A Study of Combined Interventions

This post will briefly indicate the main points made in the paper "The effect of tailored information, goal setting and tailored feedback on household energy use, energy-related behaviors, and behavioral antecedents" by Abrahamse et al.

The study examines the effects of the combination of three interventions. The three intervention types used in the study are information, goal setting and feedback. The households are provided information about energy-related problems and individualised energy conservation tips. In terms of goal setting, the experimental groups are asked to save 5% both as an individual and as a group. As for feedback, the experimental groups   received feedback about energy savings. The hypotheses being tested in the study are as follows.

The households exposed to the combination of interventions

H1A - would save more direct (gas, electricity and fuel) and indirect energy (i.e. disposal of goods),

H1B - would adopt more energy-saving behaviours,

H1C - would acquire higher level of knowledge than the control group who had not received any information and feedback. Further, the households who received group goal and group feedback are expected to

H2A - save more direct and inderect energy,

H2B - adopt more energy-related behaviours than the households who only received personal goal and feedback.

The paper proposed the results that the households exposed to the interventions saved significantly more direct energy than the control group. As for indirect energy, the difference is not significant. However, the experimental groups reduced their indirect energy use whilst the control group increased indirect energy use. Further, the experimental groups showed that they obtained more knowledge and formed more energy-saving behaviours than the control group. Moreover, the group goal and group feedback do not have an additional positive effect according to the study. 

Intervention Strategies

In this post I will summarise the review paper "A review of intervention studies aimed at household energy conversation" by Abrahamse et al.

Interventions are divided into two categories: antecedent interventions and consequence interventions. The latter intervention type is based on the presumption that the availability of positive or negative outcomes will influence behaviour. The former intervention strategies aim to influence the underlying determinants of behaviour, which in turn are assumed to affect behaviour.

Antecedent interventions: Commitment, Goal Setting, Information and Modeling.

- Commitment: An oral or written pledge or promise to change behaviour (e.g. conserve energy).

- Goal Setting: A goal can be set by experimenters or by the households (e.g. reduce energy use by %5).

- Information: General or specific information about energy-related problems or solutions are used to encourage energy conservation behaviours (e.g. workshops, mass media campaigns and home audits).

- Modeling: This method is based on providing examples of recommended behaviours which are understandable, relevant, meaningful and rewarding to the households.

Consequence interventions: Feedback and Reward.

- Feedback: It consists of providing households with the information about energy consumption or energy savings (e.g. daily feedback, comparative feedback).

- Reward: Monetary rewards are used as motivators for energy conservation.

The effectiveness of the interventions is increased when antecedent and consequence interventions are combined and used together (e.g. goal setting with feedback).

Event Detection

This post summarises the paper "Event Detection for Non Intrusive Load Monitoring" by Anderson et al.

The main aim of the paper is to introduce a set of metrics for evaluating the event detection algorithms in NILM and to provide a dataset which enables others to test their event detection algorithms. The paper also briefly discusses the methods used for event detection in the NILM (for more information about NIALM please see Oliver's blog). According to the paper, there are three main categories which aim to detect explicit change-points in a time-series signal. The categories are listed as follows.

- Expert Heuristics: The detection of step-like changes present in the power consumption signal with the use of the standard deviation of the fixed-size set of examples. 

- Probabilistic Models: As an example, the Generalized Likelihood Ratio(GLR) is introduced. It requires the parameters including the window length , the power change and a threshold so as to calculate decision statistic.

- Matched-Filters: The matching of a known signal (mask) with an unknown one in order to detect the mask in the unknown signal.

The event detection metrics introduced for event-based NILM approach are True Positive Rate, True Positive Percentage, Total Power Change, Average Power Change and  Score Function. In order to show the validity of the metrics, the paper uses a modified generalized likelihood ratio(GLR). The GLR is tested with a  fully-labeled dataset for BLUED dataset which is available here.

The paper concludes that the total power change metric outperforms the other three metrics which are True Positive Rate,  True Positive Percentage and Average Power Change.

Agent vs Direct Manipulation

In this post, I will write some points made in the article "Designing for Human-Agent Interaction" by Lewis.

There are two main interface metaphors: agent and environment. While agent metaphor uses the computer as a mediator which responds to user requests,  environment metaphor enables the user to interact with the task domain directly. Agents are designed to automate repetitive, poorly specified or complex precesses by bridging the gap between a user's goals and actions.

The figure above taken from the article shows the possible automations that can occur in the execution or evaluation phases defined by Norman. The issue of the automation is that if the execution side is automated the user may fail to monitor effects of actions and cannot regulate the incorrect ongoing processes. On the other hand, if the evaluation side is automated the user might be unable to observe the results of actions. 

* I will keep updating this post.

Occupancy Prediction

In this post, I will try to guess the hours in which property is occupied by tenants. The prediction will be performed through the use of different arbitrary thresholds in electricity consumption data. The main aim is to find time intervals, which tenants are at home, with high accuracy. For privacy issues, the owner of the data is not represented.

11 Jan 2013, Friday.

Power-consumption hours: 00.00 - 01.00, 05.00 - 08.00, 17.00 - 24.00

12 Jan 2013, Saturday.

Power-consumption hours: 00.00 - 01.00 , 05.00 - 08.00, 11.00 - 14.00, 20.00 - 21.00, 23.00 - 24.00

13 Jan 2013, Sunday.

Power-consumption hours: 00.00 - 02.00 , 08.00 - 13.00, 20.00 - 22.00

14 Jan 2013, Monday.

Power-consumption hours: 06.00 - 22.00

15 Jan 2013, Tuesday.

Power-consumption hours: 00.00 - 01.00 , 06.00 - 08.00, 18.00 - 24.00

16 Jan 2013, Wednesday.

Power-consumption hours: 00.00 - 01.00 , 06.00 - 08.00, 17.00 - 24.00

17 Jan 2013, Thursday.

Power-consumption hours: 00.00 - 01.00 , 05.00 - 08.00, 17.00 - 24.00

Please note that the hours stated above represent the time intervals in which tenants consume observable energy. Hence, we might assume that at least one occupant is at home during these time intervals. 

MatPlotLib

MatPlotLib is a plotting library which is created by John Hunter. It offers an useful object-oriented API for Python programmers. For further information please see the link : http://matplotlib.org/

A sample graph which shows the historical electricity use in a day. 

Team Energy

I completed the implementation of TeamEnergy which is a web application allowing multiple users to coordinate their energy consumption activities. The project is uploaded to the bitbucket which provides unlimited private repositories for online users. The steps followed for the process of uploading can be found in the link below.

https://confluence.atlassian.com/display/BITBUCKET/Set+up+Git+and+Mercurial

The articles I read are as follows:

- A Large-Scale Study on Predicting and Contextualizing Building Energy Usage 

In this paper, a data-driven approach is represented to model energy consumption in buildings. The monthly electricity and gass bills are used as data which is collected by a utility for several years. Both parametric and non-parametric learning methods are utilised to model energy consumptions related to the features of the buildings. Based on these models, two end-user system is offered. One is for utilities or authorised institutions, and it visualises energy consumption for each unit. The other system enables consumers to enter their own data like the features of the building and energy usage, and the system, in return, provides a comparision of similar buildings. 

* In order to compare energy consumptions of buildings, electricity and gas usages are combined to obtain equivalent units through the use of conversation factor (1/3) 29.3 therms/kwh of the US. We can use this conversion in our home energy management system.

- The Smart Thermostat: Using Occupancy Sensors to Save Energy in Homes

Basically, this paper presents a smart thermostat which adjusts set points according to occupancy patterns that are generated through several sensors.  

Finally, I finished reading the book "Plans and Situated Actions" by Suchman.