TY - GEN
T1 - A composite k-nearest neighbor model for day-ahead load forecasting with limited temperature forecasts
AU - Zhang, Rui
AU - Xu, Yan
AU - Dong, Zhao Yang
AU - Kong, Weicong
AU - Wong, Kit Po
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2016/11/10
Y1 - 2016/11/10
N2 - 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. © 2016 IEEE.
AB - 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. © 2016 IEEE.
KW - Ensemble strategy
KW - K-nearest neighbor method
KW - Load forecasting
KW - Temperature forecasts
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85002199008&origin=recordpage
U2 - 10.1109/PESGM.2016.7741097
DO - 10.1109/PESGM.2016.7741097
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781509041688
VL - 2016-November
T3 - IEEE Power and Energy Society General Meeting
BT - 2016 IEEE Power and Energy Society General Meeting, PESGM 2016
PB - IEEE Computer Society
T2 - 2016 IEEE Power and Energy Society General Meeting, PESGM 2016
Y2 - 17 July 2016 through 21 July 2016
ER -