# Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption
## 1 Introduction  
The current low fertility rate in society may threaten the future demographic dividend [1]. To increase residents' willingness to have children, the government has introduced a fertility subsidy policy that primarily benefits nuclear families [2, 3]. However, the current tariff policy for childbirth is based solely on the number of people in a household, without considering the type of nuclear family [4]. This approach may lead to imprecise benefits and wastage of resources, particularly with the increasing number of three-child families [5]. The current implementation model for the fertility subsidy policy involves residents completing a range of documents, including online declarations, signed commitments, and regular extension procedures, which make it difficult to monitor the movements of nuclear family members. As a result, the differential tariff policy in the fertility package has been slow to implement [6]. To promote the policy and enable power companies to achieve refined management and provide differentiated electricity services, it is imperative to accurately identify nuclear family types in a reasonable manner, simplify the processing process, and respond promptly to changes in nuclear family mobility [7]. Therefore, research on intelligent algorithms based on big data of electricity offers new research avenues to identify specific resident types [8]. Current research on identifying specific types of electricity users employs various classification and identification methods based on different research purposes and user features. For example, [PERSON] proposed a CHAID decision tree algorithm to identify outage-sensitive users in residential, industrial, and commercial sectors [9]. [PERSON] developed a library of electricity consumption feature indicators for empty nest electricity users, and improved the distribution recognition ability of random forest data through weighted random forest [10]. [PERSON] studied changes in the electricity consumption load features of coal-to-electricity users after heating, and developed a support vector machine model for particle swarm optimization to identify typical daily electricity consumption loads in winter [11]. [PERSON] proposed an improved Bayesian algorithm for better recognition of relatively poor households in high-rise buildings [12]. [PERSON] proposed a probabilistic model identification of quadratic programming to improve vacant users' recognition rates, while [PERSON] used a multi-model stratified fusion method to identify distribution data for 20 industry samples in Chongqing [13; 14]. [PERSON] classified users into three categories of high, medium, and low energy consumption using a BP neural network, and identified three categories of users by combining the features of electricity consumption at peak and valley level three [15]. The innovation of this paper lies in proposing a novel method for identifying nuclear families in residential electricity consumption using a deep forest algorithm [16]. The proposed method combines stratified sampling with an improved deep forest approach to determine the number of children in nuclear families based on load data. The method also uses feature selection based on the Pearson correlation coefficient and random forest importance ranking to remove features with low correlation and importance. Additionally, the proposed method balances the number of features by classifying them based on their importance and using stratified sampling to optimize the scan results. Finally, an improved cascade forest with feature input replacement base learner is trained and evaluated using accuracy evaluation metrics. The experimental results show that the proposed method outperforms the original deep forest and random forest methods, achieving a recognition accuracy of 94%. The proposed method has potential applications in power companies for improving lean management and can contribute to intelligent marketing schemes for different types of households.