Provided is a residential electricity peak-valley-flat dividing method based on data dimension increasing and K-means clustering, comprising the following steps: collecting residential daily electricity information data using a user information collection system, and getting the residential load value at each hour of each day in a statistic time period; building a data sample set of daily load values in a long time period through data dimension increasing based on the residential load values, and determining the number of samples in the data sample set; and dividing all the samples in the data sample set into a peak cluster, a flat cluster and a valley cluster using a K-means algorithm. According to the invention, by building a data sample set through data dimension increasing, the load data of each time point in a long time period can be included, and the problem that the iteration cost of the algorithm increases due to increase of days in the time period is avoided. The method is applicable to residential electricity peak-valley-flat division in a long time period, and is of guiding significance to relevant workers' time-of-use residential electricity pricing.