# A Two-Stage Household Electricity Demand Estimation Approach Based on Edge Deep Sparse Coding
## 5 Conclusions  
A novel two-stage estimation approach for household electricity demand based on edge deep sparse coding was proposed to improve the estimation accuracy of ultra-short term residential electricity demand. The deep non-negative sparse coding algorithm was implemented in the sparse coding stage to extract usage patterns. The initial matrix contained the usage state of household devices. DUBPs were used as input to the LSTM network in the forecasting stage.  
The results of the test experiments demonstrated that the proposed method can achieve considerable improvement in household-level consumption demand estimation and can substantially improve its performance at peaks and troughs. In terms of MAPE, the proposed method outperformed MLP by 51.26%, ARIMA by 36.62%, and LSTM with shallow K-SVD by 16.4%. In the field of mean absolute error and root mean squared error, the improvement was 53.95% and 36.73% compared with MLP, 28.47% and 23.36% compared with ARIMA, 11.38% and 18.16% compared with LSTM with shallow K-SVD.  
Figure 11: Actual (blue) and forecasted load profiles (red) throughout a day for the leading scenarios in five households from REFIT dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**REFIT** & **Sociodemographic Information** & **RMSE (kW)** & **MAE (kW)** & **MAPE (\%)** \\ \hline \multirow{3}{*}{REFIT House1} & 2 people & \multirow{3}{*}{21.30} & \multirow{3}{*}{17.27} & \multirow{3}{*}{24.14\%} \\  & 3 bedrooms & & & \\  & 27 equipment & & & \\ \hline \multirow{3}{*}{REFIT House2} & 2 people & \multirow{3}{*}{43.26} & \multirow{3}{*}{28.84} & \multirow{3}{*}{20.58\%} \\  & 33 equipment & & & \\ \hline \multirow{3}{*}{REFIT House3} & 3 people & \multirow{3}{*}{31.75} & \multirow{3}{*}{22.66} & \multirow{3}{*}{22.79\%} \\  & 3 bedrooms & & & \\  & 26 equipment & & & \\ \hline \multirow{3}{*}{REFIT House4} & 1 people & \multirow{3}{*}{11.87} & \multirow{3}{*}{9.74} & \multirow{3}{*}{26.53\%} \\  & 3 bedrooms & & & \\ \cline{1-1}  & 19 equipment & & & \\ \hline \multirow{3}{*}{REFIT House5} & 4 people & \multirow{3}{*}{93.21} & \multirow{3}{*}{57.26} & \multirow{3}{*}{21.76\%} \\  & 4 bedrooms & & & \\ \cline{1-1}  & 44 equipment & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Forecasting performance in five houses of REFIT using the proposed method.  
Future work can focus on improving the estimation accuracy in two aspects. One is to continue exploring the hidden information of the household load or electrical equipment load to extract more effective behavior patterns. The other is to further utilize the potential of the proposed deep learning network by solving the overfitting issue of deep learning or adding other external factors, such as temperature, relative humidity, and their higher-order terms and interactions to establish a deep learning model that is more suitable for household load prediction.  
Conceptualization, Y.L., Y.S. and B.L.; formal analysis, Y.L. and B.L.; writing-original draft, Y.L.; writing-review & editing, Y.L., Y.S. and B.L.  
This work was funded by National Natural Science Foundation of China (Grant No. 51777068) and Fundamental Research Funds for the Central Universities (Grant No. 2019 QN110).  
The authors declare no conflicts of interest.