A clustering and weighted euclidean norm-based short-term load forecasting of normal and anomalous days

Abstract

The paper presents a clustering and the weighted Euclidean distance measure-based combined approach for short-term load forecasting (STLF) of a day ahead hourly load of normal and anomalous days. Hourly load, temperature, humidity and day type are selected as the key variables of the data-set used. The input data-set for the clustering algorithm follows distinct day-type similarity criterion for normal and anomalous days. The model uses a two-tier architecture. In tier one, the input data-set is grouped into a pre-defined number of cluster subsets using weather parameter similarity. In tier two, the forecast day cluster subset is narrowed down to obtain five similar days of the forecast day using weighted Euclidean distance norm. The hourly loads of the similar days are then averaged to obtain the actual hourly load forecasts. The technique is tested for the eleven electrical load zones of the New York Independent System Operator (NYISO) over a period of three years. The performance evaluation of the proposed approach is done in comparison with the conventional neural network-based model implemented by the NYISO. Mean absolute percentage error (MAPE) is taken as the performance measure. The proposed technique gives lower MAPE values for all day types, seasons and terrains.