Short-term focast of load; Similar shape algorithms; Machine learning model; Electricity market.


 This paper presents a method based on machine learing model to forcast daily electrical loads. The model requires only historical datasets of load data. Hence, it is simple and more available to implement. The algorithm model “K-nearest neighbor - Regression” is used to predict the power load for 24 hours of the next day by looking in historical data set of days with weak factors most similar to the next day and using the load of those days to calculate and forecast the load in day ahead. Finally, data of Italia is used to verify the proposed model. The resulting error performanced by this model is calculated and compared with Terna's forecast. In addition, the introduced method is realized by applying to Italy and data of GME (electricity market data in Italy).


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How to Cite

Nguyen Huu, Đức. “SHORT-TERM FORECAST OF LOAD BY MACHINE LEARNING MODEL: APPLICATION TO ITALIA”. Journal of Military Science and Technology, no. 74, Aug. 2021, pp. 52-58,



Research Articles