Recently there has been a significant proliferation in the use of forecasting techniques,
mainly due to the increased availability and power of computation systems and, in particular …

Existing studies on 1-24 hr load forecasting algorithms are reviewed, and an expert-system-
based algorithm is presented as an alternative. The logical and syntactical relationships …

This paper discusses the state of the art in short-term load forecasting (STLF), that is, the
prediction of the system load over an interval ranging from one hour to one week. The paper …

Weather is a key driving factor of electricity demand. During the past five decades,
temperature is the most commonly used weather variable in load forecasting models …

This paper presents a highly adaptable and robust short-term load forecasting algorithm
developed using hybrid modeling techniques. Adaptive general exponential smoothing …

A review of forecasting procedures which have been developed in the electricity supply
industry to serve the short-term needs of load dispatching is presented. The procedures are …

The problem of short-term load forecasting that is the prediction of the system load over an
interval of time from 1 hour to 1 week is studied. An adaptive model to forecast hourly electric …

Accurate load forecasting is essential for power system planning and operation. As the
electricity end uses expand, load forecasting technique becomes more complicated …

Recurrent neural networks (RNN) model time series by feeding back the representation from
the previous time instant as an input for the current instant along with exogenous inputs. Two …

A robust model for forecasting power system hourly load is developed. The method exploits
the convenience of the autocorrelation function (ACF), and the partial autocorrelation …