Power Consuming Activity Recognition in Home Environment Edinburgh Napier University Xiaodong Liu and Qi Liu

Contents 1 2 3 4 5 Background Related Work Research Methods and Ideas Experiment Results 5 Conclusion and Future Work

Background Electric energy is one of the most widely used energy sources in mankind, and the energy consumption of residents has become the second largest energy-consuming sector after the industrial sector. Some objective factors such as weak consumer awareness, old household electricity facilities and obsolete energy-saving technology, resulting in a serious waste of electricity and energy.

Background Power Consuming Activity Recognition Operation and Management of Electric Industry Energy Saving and Emission Reduction Development of Smart Home Users' Energy Consumption Behavior Change

Activity recognition system Related Work Activity recognition system 2. Wearable devices 1.Camera Intrusive, privacy, installation cost Must carry, battery, detection of simple action Different Approaches to data collection 3. Radio equipment 4. Sensor network The platform is expensive, only detects simple gestures or actions Let users perform activities in a comfortable manner

Activity recognition based on a Sensor Network Related Work Activity recognition based on a Sensor Network Knowledge-driven：Based on an ontology Need a clear field of knowledge The accuracy of knowledge definition determines the accuracy of recognition; Difficult to adapt to all situations Activity Recognition Data-driven： Based on probability model (CRF，HMM)； Based on data mining (KNN,SVM，NB) Need a lot of training processes No use of domain knowledge

Data Collection Hardware Research Methods and Ideas Data Collection Hardware Smart socket Power metering module: HLW8012 from ShenZhen Heliwei company， Support 50 / 60Hz IEC 687/1036 standard accuracy requirements. WIIF module: ESP8266 from Shanghai Lexin company. Collect information of appliances to recognize activity.

Knowledge-driven: Get context location information Research Methods and Ideas Knowledge-driven: Get context location information

Knowledge of activity ontology Research Methods and Ideas Knowledge of activity ontology rule1 Irrelevant to time point and duration rule2 Irrelevant to element action order Activity-Location Dependency: The logical language is described as: Activity ∀ hasLocation (Balcony or Bathroom or Bedroom or Diningroom or Kitchen or Sittingroom or OutOfHome) Activity ∃ hasLocation Locations. So we generated an Activity-Location matrix: rule3 Pra=

Research Methods and Ideas Activity ontology use "who", "where", "when", “what" to describe power consumption activities, mainly contains person, location, sensor, appliance and activity information dimensions.

Data+Knowledge-driven Research Methods and Ideas Data+Knowledge-driven （3）Recursion ，to t=3,4,…, T-1 𝛿 𝑡 𝑖 = 1≤𝑗≤𝑄 𝑚𝑎𝑥 𝛿 𝑡−2 𝑗 𝛿 𝑡−1 𝑘 𝑎 𝑗𝑘𝑖 𝑏 ki 𝑥 𝑡 , 𝑖=1,2,…,𝑄 ψ 𝑡 𝑖 =𝑎𝑟𝑔 1≤𝑗≤𝑄 𝑚𝑎𝑥 𝛿 𝑡−1 𝑗 𝑎 𝑗𝑘𝑖 ,𝑖=1,2,…,𝑄 （4）Terminate ， 𝑃 ∗ = 1≤𝑗≤𝑄 𝑚𝑎𝑥 𝛿 𝑇 𝑖 𝑖 𝑇 ∗ =𝑎𝑟𝑔 1≤𝑖≤𝑄 𝑚𝑎𝑥 𝛿 𝑇 𝑖 （5）Optimal path backtracking ，to t=T-1, T-2,…, 1 𝑖 𝑡 ∗ = ψ 𝑡+1 𝑖 𝑡+1 ∗ Second-order HMM (HMM2) hypothesis: a) state of time t, not only depend on the state of time t-1, but also depend on the state of time t-2; b)The observed variable 𝑥 t at time t is related not only to the current state of the system but also to the previous state of the system. HMM2 Viterbi algorithm ： （1）Initialize， 𝛿 1 𝑖 = π 𝑖 𝑏 𝑖 𝑥 1 , 𝑖=1,2,…,𝑄 ψ 1 𝑖 =0,𝑖=1,2,…,𝑄 （2）t=2， 𝛿 2 𝑖 = 1≤𝑗≤𝑄 𝑚𝑎𝑥 𝛿 𝑡−1 𝑗 𝑎 𝑗𝑖 𝑏 𝑗𝑖 𝑥 2 , 𝑖=1,2,…,𝑄 ψ 2 𝑖 =𝑎𝑟𝑔 1≤𝑗≤𝑄 𝑚𝑎𝑥 𝛿 𝑡−1 𝑗 𝑎 𝑗𝑖 ,𝑖=1,2,…,𝑄 HMM2K Viterbi algorithm： Join the Activity-Location matrix Pra and user’s Loc, Search space is not S={1,2,…,Q}，the search space is S’=S.*Pra(Loc(n),:)

Data-driven method: HMM2 Experiment Results Data-driven method: HMM2 Precision= 1 𝐶 𝑖=1 𝐶 𝑇𝑃 𝑖𝑖 𝑁𝐼 𝑖 Recall= 1 𝐶 𝑖=1 𝐶 𝑇𝑃 𝑖𝑖 𝑁𝑇 𝑖 The experiment was first performed on the smart home activity data set established by Kasteren et al. To detect the effectiveness of the second order HMM, which compared the Naive Bayesian (NB) and the first order HMM. Evaluation parameters Accuracy= 𝑖=1 𝑄 𝑇𝑃 𝑖𝑖 𝑇𝑜𝑡𝑎𝑙 F1= 2∗𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛∗𝑟𝑒𝑐𝑎𝑙𝑙 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑟𝑒𝑐𝑎𝑙𝑙   Precision Recall F-measure Accuracy NB 40.9 38.9 39.8 67.9 HMM 48.3 63.1 54.7 81.0 HMM2 50.2 64.8 56.6 82.1 Analysis: HMM2’s performance is better than NB and HMM1, overall recognition accuracy compared to HMM1 have improved.

Data- and Knowledge-driven method Experiment Results Data- and Knowledge-driven method Experiments on our own data set, we collected data of 7 days, activities recorded by self-recording way.   Precision Recall F-measure Accuracy Time NB 55.1 59.2 57.7 76.4 0.11 HMM 69.3 70.3 69.7 85.3 0.39 HMM2 70.4 71.8 71.1 88.0 0.51 Proposed method 79.9 84.2 82.0 95.8 0.27 Analysis: The static data-driven method is faster, but the accuracy is low, proposed method’s accuracy is better than other methods, and the algorithm cost is much shorter than HMM2.

Conclusion and Future Work In this paper, we use the low-order probability model to recognize the activity. Whether the n-order probability model will increase the accuracy is the research direction of our next stage. Mining more domain and prior knowledge, single-user and multi-user behavior to help us better sort and combine these activities, so as to more accurately recognize these activities.

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