###### Abstract  
Daily load forecasting is the basis of the economic and safe operation of a power grid. Accurate prediction results can improve the matching of microgrid energy storage capacity allocation. With the popularization of smart meters, the interaction between residential electricity demand and sources and networks is increasing, and massive data are generated at the same time. Previous forecasting methods suffer from poor targeting and high noise. They cannot make full use of the important information of the load data. This paper proposes a new framework for daily load forecasting of group residents. Firstly, we use the singular value decomposition to address the problem of high dimensions of residential electricity data. Meanwhile, we apply a K-Shape-based group residential load clustering method to obtain the typical residential load data. Secondly, we introduce an empirical mode decomposition method to address the problem of high noise of residential load data. Finally, we propose a Bi-LSTM-Attention model for residential daily load forecasting. This method can make full use of the contextual information and the important information of the daily load of group residents. The experiments conducted on a real data set of a power grid show that our method achieves excellent improvements on five prediction error indicators, such as MAPE, which are significantly smaller than the compared baseline methods. daily load forecasting; group resident clustering; Bi-LSTM; self-attention mechanism +
Footnote †: journal: Applied  
1
Footnote †: journal: Applied  
1
Footnote 1: School of Computer Science, Northeast Electric Power University, Jilin City 132012, China 1, [PERSON]  
1 School of Computer Science, Northeast Electric Power University, Jilin City 132012, China 1, [PERSON]  
1 School of Information Engineering, Chuangchun Sci-Tech University, Changchun 130012, China 1, [PERSON]  
1 School of Computer Science, Northeast Electric Power University, Jilin City 132012, China 1, [PERSON]  
1 School of Information Engineering, Chuangchun Sci-Tech University, Changchun 130012, China 1, [PERSON]  
1 School of Computer Science, Northeast Electric Power University, Jilin City 132012, China 1, [PERSON]  
1 School of Information Engineering, Chuangchun Sci-Tech University, Changchun 130012, China 1, [PERSON]
Autoregressive Integrated Moving Averages, Seasonal Autoregressive Integrated Moving Averages, and Vector Autoregressive Moving Averages [8]. ARIMA and SARIMA are suitable for univariate load forecasting [9; 10]. VARMA is suitable for multivariate time series forecasting, and it requires a high stability of the series [11]. Traditional time series models are considered linear models, while the load forecasting problem is nonlinear. Recently, more and more researchers prefer to use artificial intelligence methods on load forecasting problems. Artificial intelligence prediction methods include support vector machines, artificial neural networks, etc. However, existing microgrid daily load forecasting research mainly focuses on the power generation side, for example, on the prediction of thermal power and solar lamp power generation equipment. There is insufficient research on short-term load forecasting on the electricity side. Research on residential load forecasting often focuses on residential load forecasting for individual households, and there is a lack of research on group residential load forecasting. For example, one study [12] used wavelet transform and support vector machines to predict the time series of residential loads. However, the above methods are for load forecasting of individual residents. The authors did not take into account the situation of the population of the group. Another study [13] proposed a linear regression short-term load forecasting model considering the influence of working time and meteorological factors, but its error is relatively large.