# A Household Energy Efficiency Index Assessment Method Based on Non-Intrusive Load Monitoring Data
## 2 Household Energy Efficiency System Architecture and Data
### Data  
At present, power load monitoring is divided into intrusive and non-intrusive approaches. Non-intrusive load monitoring is simple to install and makes it convenient to collect and analyze data. NILM technologies are becoming mature with the improvement of advanced metering infrastructure (AMI). Load identification, for example, uses load signal decomposition to non-intrusively obtain load information [23]. Figure 2 shows the energy efficiency assessment structure based on NILM. Figure 1: Residential user energy efficiency assessment framework. Figure 2: Energy efficiency assessment structure diagram based on NILM technology. The NILM equipment (Tianjin Qiushi TransEnergy Technologies Co., Ltd., Tianjin, China) is installed at the entrance of the home grid gateway, and the Hidden Markov Model (HMM) is used to model the total load and its internal electrical equipment. Therefore, the NILM problem can be expressed as a decoding problem of HMM and then solved by the improved Viterbi algorithm [24]. This method can reduce the number of state sequences that the ordinary Viterbi algorithm needs to traverse and calculate the complexity. The NILM information is subjected to energy efficiency analysis after principal component analysis (PCA) and standardization processing. Finally, household equipment power consumption and new energy consumption strategies can be obtained through the analysis. At present, the NILM can detect the energy consumption of 51 household appliances. However, NILM equipment suppliers will continuously analyze the characteristics of electrical appliances on the market and update the electrical appliance feature library. If a new appliance is added, the NILM equipment supplier will match the feature library. By constantly updating the NILM feature library, more electrical appliances will be identifiable. If a new appliance is not identified, NILM will alert the equipment supplier, and then the equipment supplier will carry out feature analysis and add it to the feature library. Non-intrusive information data is subjected to energy efficiency analysis through normalization and PCA. First, it is necessary to classify the users. Then, the entropy weight method is used to combine the same type of electrical data, and the energy consumption of the distributed power source is determined to calculate the index. Finally, power use suggestions are generated according to the results of the comprehensive evaluation. The eigenvalues obtained from the steady-state load current include the peak value, standard deviation (SD), root mean square (RMS) value, and the number of wave peaks (valleys). These eigenvalues can be used as the load signature (LS) characteristics and provide a vital basis for load identification. Waveform and eigenvalues from the steady-state load current of some types of home appliance equipment are shown in Figure 3 (the red line is the wave peak, the black line is the wave trough, the green line is the RMS, and the yellow line is the SD). Table 1 shows the characteristic values of each load. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Fan**} & \multirow{2}{*}{**Heater**} & \multicolumn{2}{c}{**Microwave**} & \multirow{2}{*}{**Electric**} & \multirow{2}{*}{**TV**} & \multicolumn{2}{c}{**Number of**} \\  & & & & **Oven** & & **Kettle** & & **Wave Peaks** \\ \hline Max & 0.0178 & 0.1999 & 0.4049 & 0.5450 & 0.1209 & 3 \\ Min & \(-\)0.0115 & \(-\)0.1937 & \(-\)0.4484 & \(-\)0.5388 & \(-\)0.1096 & 3 \\ RMS & 0.0076 & 0.1377 & 0.2542 & 0.3845 & 0.0594 & 6 \\ SD & 0.0071 & 0.1378 & 0.2543 & 0.3848 & 0.0594 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristic values of the steady-state operation. Figure 3: Waveform and characteristic values of the steady-state load current of some types of household appliances. The maximum, minimum, RMS, and SD of current can be used as LS characteristics of the load to identify a single load.