Modeling and Forecasting Electricity Loads and Prices

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Merit-order effects of renewable energy and price divergence in California’s day-ahead and real-time electricity markets (2016, background): “Further, existing literature indicates that these variables with relatively small hour-to-hour and day-today changes can be accurately forecast via time-series modeling (e.g., ARIMA) in the day-ahead time frame (Weron, 2006).”
A bilevel model for electricity retailers' participation in a demand response market environment (2013, background): “The interested reader is referred to Dubrovsky (1997) and Madsen (1985) and to Weron (2006) and Jónsson (2012), respectively for an introduction to modelling of stochastic processes related to weather and electricity markets.”
Pattern-based local linear regression models for short-term load forecasting (2016, methodology): “Among the conventional STLF methods the most commonly employed are: the HoltWinters exponential smoothing (ES) and the autoregressive integrated moving average (ARIMA) models [1].”
A review of electricity product differentiation (2014, background): “Since spot electricity prices are highly volatile with occasional spikes [57–68], LSEs and customers may seek to manage electricity spot-price risk using such hedge instruments as, tolling agreements, forward contracts, and capacity options that are traded in financial markets [69–74].”