In this chapter, we have described the vision of the smart grid, discussed smart meters and the use of DSM techniques. We detailed the challenge for standard DSM to manage electricity demand on a large scale (thousands to millions smart meters) and discussed how agent-based approaches could help meet this challenge. We also discussed how recent agent-based DSM approaches have simplified the user’s settings (Requirement 1) and have not considered the dependencies between appliances (Requirement 2) and hence are unlikely to be widely adopted. Moreover we identified the need to predict appliance usage in order to maintain user comfort.
We then discussed existing approaches for modelling and predicting inter-dependencies in time-series data. We described two popular approaches for modelling dependencies
Chapter 2 Literature Review 29
between events in temporal data: i) Temporal Point Process, ii) a rule-based method based on Frequent Episode Discovery. In particular, we use the state–of–the–art algo-rithms: PCIM (for a temporal point process approach), and EGH (a rule-based method) as benchmarks to compare the appliance usage prediction accuracy in Chapter 6. How-ever, these approaches do not take into account human routine (Requirement 1), and thus we expect them to fail in predicting appliance usage (Requirement 3). Indeed we demonstrate this later in Chapter 6.
Building upon this, we discussed existing work for modelling and predicting human routine in some different applications such as location prediction, mobility prediction, and activity recognition. We use non-parametric method, DPM, as our benchmarks for predicting human routine. One of the popular models for detecting human routine is DPM. However, DPM only deals with one single appliance sequence, and does not take into account the dependencies between multi-appliances (Requirement 2). Therefore, the usage prediction accuracy will not be accurate (Requirement 3).
Lastly, we provided background of probabilistic graphical models such as HMMs which will be used in Chapter 4. We also discussed two main approximate inference methods:
Gibbs sampling and variational bayes that will be discussed and used in Chapter 5.
To develop algorithms that address the research requirements, more importantly, we first extend the rule-based method by considering capturing the human’s routine, and thus propose a rule-based algorithm, EGH-H (see Chapter 4). The EGH-H algorithm is mainly designed for scenarios in which notifications are needed in a short time. Again, we will demonstrate its effectiveness in Chapter 6. To model the more accurate appliance usage prediction, we propose our graphical model based, GM-PMA (see in Chapter 5) that also captures both human routine and the inter-dependencies between appliances.
We will also show that GM-PMA dominates other benchmarks for accurate prediction in Chapter 6. Both algorithms, GM-PMA and EGH-H, takes into account the cyclic behaviour of users following the work of Gonz´alez et al. (2008). They have found strong periodicities in human location behaviour, and from common sense about how people use appliances in the home, it is reasonable to assume that such periodicities are true for appliance usage. Hence, we assume that human behaviour in terms of appliance usage forms cycles of weekly periods. We will show later about how this assumption is validated by the model performs well empirically (see Chapter 6).
In order to objectively compare the accuracy of various algorithms for predicting appli-ance usage, it is necessary to test them on real–world dataset. The following chapter describes the appliance usage prediction model, and discuss real–world dataset that we will use for evaluation.
Chapter 3
Appliance Usage Prediction Model and Datasets
This chapter describes the model of appliance usage prediction problem and the real–
world datasets that we use for evaluating our proposed algorithms in the next few chapters. We start by modelling the appliance usage in the home, and discuss inter–
dependencies between the appliances as well as the cycle of human routine for using the appliances in Section 3.1. Next, we provide two types of real–world datasets that will be used for evaluating the performances of the algorithms later in Chapter 6: i) expensive deployment for data collection, but low level of uncertainty; ii) cheap deployment for data collection, but high level of uncertainty. We describe the preparation of the datasets in more detail in Section 3.2. Finally, Section 3.3 summarises the findings of this chapter.
3.1 Model Description
Our goal is to generate time–specific predictions of appliance usage based on historical behaviour. In more detail, given a set of training data of past behaviour consisting of a time context indicating the day of the week and appliance usage, we wish to predict which appliances are likely to be used, and when they are likely to be used during the day. Assume that we have a finite set of appliance usage events, where different types of appliances are distinguished by 𝑙 ∈ 𝐿. We consider a typical domestic profile that spans the usage over a day divided in 𝑇 slots represents by a set of time slots 𝑡 ∈ 𝑇 (e.g., if T = 24, the day is divided into one-hourly slots). We are concerned with modelling discrete binary information, 𝑥𝑛,𝑙,𝑡 ∈ {0, 1}, indicating whether appliance 𝑙 will be used on day 𝑛 at time 𝑡. Let 𝑥𝑛,𝑙 = ⟨𝑥𝑛,𝑙,1, . . . , 𝑥𝑛,𝑙,𝑇⟩ denote the observations of appliance 𝑙 on the 𝑛𝑡ℎ day. Then, 𝑥𝑛 is the observation of a subset appliances 𝐿 on the 𝑛𝑡ℎ day. In probabilistic terms, we need to find the conditional probability 𝑝(𝑥𝑛,𝑙,𝑡∣X, 𝑛, 𝑙, 𝑡), where X = {𝑥1,𝑙, . . . , 𝑥𝑛−1,𝑙} represents the history of the use of appliances. To solve this
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problem, we need to take into account two main dependencies that underlie a consumer’s activities:
∙ Time dependencies: consumers can undertake their activities at different times that satisfy their needs and daily routine. For example, if one uses a washing machine at time 𝑡, it is less likely one will use it again at time (𝑡 + 1) as one wash implies a reduction in the number of dirty clothes.1
∙ Activity inter–dependencies: some types of activities may depend on other activities. For example, while cooking, one might need to use the oven and mi-crowave, then one might need to use the dishwasher to wash the dirty dishes.
Therefore, the activities of using the oven, microwave, and dishwasher are inter–
dependent.
In Chapter 4 and 5, we develop algorithms that take into account these dependencies of appliance usage. In the next section, we analyse the set of real–world datasets that we use to evaluate our algorithms in Chapter 6.