Load forecasting is an important task in power system operations. Considering the strong correlation between electricity load demand and weather condition, the temperature has always been an input for short-term load forecasting. For day-ahead load forecasting, the whole next-day's temperature forecast (say, hourly or half-hourly forecast) is however sometimes difficult to obtain or suffering from uncertain forecasting errors. This paper proposes a k-nearest neighbor (kNN)-based model for predicting the next-day's load with only limited temperature forecasts, namely minimum and maximum temperature of a day, as the forecasting input. The proposed model consists of three individual kNN models which have different neighboring rules. The three are combined together by tuned weighting factors for a final forecasting output. The proposed model is tested on the Australian National Electricity Market (NEM) data, showing reasonably high accuracy. It can be used as an alternative tool for day-ahead load forecasting when only limited temperature information is available.
|Title of host publication||2016 IEEE Power and Energy Society General Meeting|
|Publisher||IEEE, Institute of Electrical and Electronics Engineers|
|Publication status||Published - 10 Nov 2016|
|Event||2016 IEEE Power and Energy Society General Meeting, PESGM 2016 - Boston, United States|
Duration: 17 Jul 2016 → 21 Jul 2016
|Conference||2016 IEEE Power and Energy Society General Meeting, PESGM 2016|
|Period||17/07/16 → 21/07/16|