The rapid transformation of the electricity sector increases both the opportunities and the
need for Data Analytics. In recent years, various new methods and fields of application have …

Electricity prices have rather complex features such as high volatility, high frequency,
nonlinearity, mean reversion and non-stationarity that make forecasting very difficult …

Regarding the complex behaviour of price signalling, its prediction is difficult, where an
accurate forecasting can play an important role in electricity markets. In this paper, a feature …

Traditionally, short-term electricity price forecasting has been essential for utilities and
generation companies. However, the deregulation of electricity markets created a …

Wind power prediction is a widely used tool for the large-scale integration of intermittent
wind-powered generators into power systems. Given the cubic relationship between wind …

In this paper, a new feature selection and forecast engine is presented for day ahead
prediction of electricity prices, which are so valuable for both producers and consumers in …

The forecasting of the day-ahead electricity price (DAEP) has become more of interest to
decision makers in the liberalized market, as it can help optimize bidding strategies and …

Artificial neural networks (ANNs) have been widely applied in electricity price forecasts due
to their nonlinear modeling capabilities. However, it is well known that in general, traditional …

This paper compares applications of scenario-based and interval optimization approaches
to stochastic security-constrained unit commitment (Stochastic SCUC). The uncertainty of …

This paper presents a method to forecast the probability distribution function (PDF) of the
generated power of PV systems based on the higher order Markov chain (HMC). Since the …