DE HUSOS E HILOS
6.2. El sujeto: una topografía mental
Having evaluated the prediction accuracy of the algorithms, we now turn to evaluate the running time of each algorithm. In particular, we run the algorithms on an Intel(R) Xeon(R) computer (64–bit operating system) with 2.67 GHz and 12GB. The results measured in seconds are depicted in Table 6.1. We measure the runtime of each algorithm on the REDD dataset (house 1) within 8 appliances. We can see that even in the case of 8 labels, the runtime of the benchmark algorithms is significant (9, 20, 14, 6 and 21 minutes for GM-PMA, DP, DP-Ext, PCIM, and EGH). In contrast, the runtime of EGH–H still remains under 3 seconds. This implies that EGH–H could be you for scenarios that feedback may need to generate in short notice. Additionally, EGH-H could be used for interactive feedback, where the agent suggests users different home energy consumption plans in real–time (if needed), as it can use EGH–H to quickly predict the next–day usage, based on the real–time feedback of users. On the other hand, GM-PMA may provide more accurate schedule of appliance usage, but this algorithm requires more time to generate. Thus, it is best use for scenarios that users are comfortable of waiting.
For example, the agent can request GM-PMA algorithm to run at the end of the day, then provide users the appliance usage plan in the morning. We now summarise the findings of this chapter in the following section.
60 Chapter 6 Empirical Evaluation
6.6 Summary
In this chapter, we provided the experimental settings for our evaluation on the real–
world datasets. We described a set of benchmark algorithms that we used to compare to our algorithms, including PCIM, DP, DP-Ext and EGH (these algorithms have also described in Chapter 2). We also provided evaluation metrics such as ROC curves and AUC to measure the prediction performances for the prediction algorithms. We then simulated the synthetic dataset with two appliances (a washing machine and a tumble dryer) to evaluate the prediction performance of all the algorithms. The use of syn-thetic data allows us to test with different settings of inter-dependencies between these two appliances. We found that GM-PMA dominated other algorithms using the syn-thetic datasets. Then, we demonstrated the prediction performance of our algorithms with other benchmark algorithms using the two real-world datasets: the REDD dataset and the FE dataset. We found that GM-PMA algorithm outperformed existing meth-ods by up to 41% in term of prediction accuracy. Similarly, EGH-H dominates other benchmarks by up to 420% faster in term of runtime performance.
So far, we have addressed the appliance usage prediction problem by proposing efficient algorithms: GM-PMA, and EGH-H. In the next chapter, we show how to use our pre-diction algorithms, and provide insights about how the agent can assist to minimise the user’s discomfort and costs as a result of deferring appliance usage.
Chapter 7
Intelligent Demand Response
In this chapter, we study a scenario in which an agent assists to optimise their savings and comfort using GM-PMA. In particular, we propose an Intelligent Demand Response (IDR) mechanism that extends GM-PMA to learn, from the user’s responses, the best suggestions to give to the user to maximise the acceptance of the load deferment. The IDR mechanism has three main phases, include: i) Appliance usage prediction (Phase 1), ii) Suggestion optimisation (Phase 2), and iii) Human-Agent interaction (Phase 3). We describe these phases more formally in Section 7.1. In order to evaluate IDR (particu-larly for Phase 3), we model and simulate realistic human responses to suggestions in Section 7.2. In Section 7.3, we elaborate on Phase 2 to show how the agent can op-timise personalised suggestions. Then, we describe how the agent can learn the user’s preferences (i.e., the trade–off between the user’s comfort and savings) from the user’s responses (i.e., Phase 3) in Section 7.4. Given this, we empirically evaluate the IDR mechanism using the REDD dataset in Section 7.5. Finally, we summarise this chapter in Section 7.6.
7.1 The IDR Mechanism
The IDR mechanism iteratively runs through the three main following phases:
∙ Appliance usage prediction (Phase 1): here, we wish to predict which appli-ances are likely to be used, and when they are likely to be used during the day.
This prediction problem has been analysed and addressed in Section 3. In particu-lar, within the home, there is a finite set of appliance usage events, where different types of appliances are distinguished by labels 𝑙 ∈ 𝐿, and the time of the day 𝑡 ∈ 𝑇 can be discretised by a number of time slots for a day (e.g., T = 24 is equivalent to 1 hour time slot). 𝑥𝑛,𝑙,𝑡 indicates whether appliance 𝑙 was used on day 𝑛 at time 𝑡.
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62 Chapter 7 Intelligent Demand Response
Here, we employ the GM-PMA algorithm to solve this problem given its superior performance in terms of accuracy.1
∙ Suggestion optimisation (Phase 2): by considering the predicted activities of the user (based on Phase 1) as well as the electricity prices (here we assume a dynamic pricing regime (Ramchurn et al., 2011a)), the agent can optimise a suggestion plan of the use of appliances that will be passed to the user to maximise their savings and comfort. In particular, we denote 𝑝𝑡𝑛∈ 𝑅 as the electricity price at time slot 𝑡 on day 𝑛. According to the electricity price, then the agent estimates optimal suggestions that can help the user save money. A suggestion is in the form of a tuple < 𝑆(𝑙)𝑡𝑛𝑖→𝑡𝑗, Δ𝑡, Δ𝐸(𝑙)𝑡𝑛𝑖→𝑡𝑗 >, where 𝑆(𝑙)𝑡𝑛𝑖→𝑡𝑗 indicates an appliance 𝑙 on day 𝑛 should be deferred from time slot 𝑡𝑖 to time slot 𝑡𝑗, Δ𝑡 = ∣𝑡𝑖 − 𝑡𝑗∣, and Δ𝐸(𝑙)𝑡𝑛𝑖→𝑡𝑗 ∈ 𝑅+ is potential savings gained if the user would accept this suggestion. We describe this phase in more detail in Section 7.3.
∙ Human-Agent interaction (Phase 3): the agent chooses the best personalised suggestions, and sends them to the user. For example, the agent may suggest the deferment of the washing machine from 8.00am to 10.00pm because it is cheaper to do so. Then, the user responds to the agent’s suggestion by agreeing or rejecting the proposal. Formally, a user’s response given to the suggestion 𝑆(𝑙)𝑡𝑛𝑖→𝑡𝑗 is noted as 𝐷(𝑙)𝑡𝑛𝑖→𝑡𝑗 = (0, 1), where 0 means the user does not follow the agent’s suggestion, and 1 means the user would follow the agent’s suggestion. Crucially, by analysing many such responses over time, the agent can learn the user’s preferences from her reactions in order to select better suggestions in Phase 2. The details of this phase are described in Section 7.4.
Now, in order to evaluate IDR, we need to model and simulate realistic human responses to suggestions (in Phase 3). Hence, we describe human response model in the next section.