| CPC G06F 17/18 (2013.01) [F24F 11/47 (2018.01); F24F 11/56 (2018.01); F24F 11/63 (2018.01); G06N 20/00 (2019.01); H04L 67/125 (2013.01); F24F 2140/60 (2018.01)] | 19 Claims |
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1. A time-series data prediction method comprising:
collecting, by a processor, a first time-series data including a power consumption data for a preset first time period of an air conditioner;
inputting, by the processor, the first time-series data including invalid data into a time-series model stored in a memory to obtain a first plurality of prediction data including a prediction data corresponding to the invalid data;
replacing, by the processor, the invalid data included in the first time-series data with preset valid data;
inputting, by the processor, the first time-series data including the valid data into the time-series model to obtain a second plurality of prediction data including a prediction data corresponding to the replaced valid data;
obtaining, by the processor, a first similarity between the first plurality of prediction data and a plurality of reference data and a second similarity between the plurality of second plurality of prediction data and the plurality of reference data;
collecting, by the processor, a second time-series data including a power consumption data for a second time period after the first time period of the air conditioner; and
determining, by the processor, whether to replace invalid data included in the second time-series data with a preset valid data by comparing the first similarity with the second similarity,
wherein the invalid data is null data corresponding to power consumption less than or equal to a preset reference value, and the valid data is data other than the null data.
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