Residential Electricity Classification Method Based On Cloud Computing Platform and Random Forest

With the rapid development and popularization of new-generation technologies such as cloud computing,big data,and artificial intelligence,the construction of smart grids has become more diversified.Accurate quick reading and classification of the electricity consumption of residential users can provide a more in-depth perception of the actual power consumption of residents,which is essential to ensure the normal operation of the power system,energy management and planning.Based on the distributed architecture of cloud computing,this paper designs an improved random forest residential electricity classification method.It uses the unique out-of-bag error of random forest and combines the Drosophila algorithm to optimize the internal parameters of the random forest,thereby improving the performance of the random forest algorithm.This method uses MapReduce to train an improved random forest model on the cloud computing platform,and then uses the trained model to analyze the residential electricity consumption data set,divides all residents into 5 categories,and verifies the effectiveness of the model through experiments and feasibility.

Improved Collaborative Filtering Recommendation Based on Classification and User Trust

When dealing with the ratings from users,traditional collaborative filtering algorithms do not consider the credibility of rating data,which affects the accuracy of similarity.To address this issue,the paper proposes an improved algorithm based on classification and user trust.It firstly classifies all the ratings by the categories of items.And then,for each category,it evaluates the trustworthy degree of each user on the category and imposes the degree on the ratings of the user.Finally,the algorithm explores the similarities between users,finds the nearest neighbors,and makes recommendations within each category.Simulations show that the improved algorithm outperforms the traditional collaborative filtering algorithms and enhances the accuracy of recommendation.

An Evaluation of Chinese Human-Computer Dialogue Technology

The human-computer dialogue has recently attracted extensive attention from both academia and industry as an important branch in the field of artificial intelligence(AI).However,there are few studies on the evaluation of large-scale Chinese human-computer dialogue systems.In this paper,we introduce the Second Evaluation of Chinese Human-Computer Dialogue Technology,which focuses on the identification of a user’s intents and intelligent processing of intent words.The Evaluation consists of user intent classification(Task 1)and online testing of task-oriented dialogues(Task 2),the data sets of which are provided by iFLYTEK Corporation.The evaluation tasks and data sets are introduced in detail,and meanwhile,the evaluation results and the existing problems in the evaluation are discussed.