Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models
Bartosz Uniejewski () and
Rafał Weron ()
Energies, 2018, vol. 11, issue 8, 1-26
Recent electricity price forecasting (EPF) studies suggest that the least absolute shrinkage and selection operator (LASSO) leads to well performing models that are generally better than those obtained from other variable selection schemes. By conducting an empirical study involving datasets from two major power markets (Nord Pool and PJM Interconnection), three expert models, two multi-parameter regression (called baseline ) models and four variance stabilizing transformations combined with the seasonal component approach, we discuss the optimal way of implementing the LASSO. We show that using a complex baseline model with nearly 400 explanatory variables, a well chosen variance stabilizing transformation (asinh or N-PIT), and a procedure that recalibrates the LASSO regularization parameter once or twice a day indeed leads to significant accuracy gains compared to the typically considered EPF models. Moreover, by analyzing the structures of the best LASSO-estimated models, we identify the most important explanatory variables and thus provide guidelines to structuring better performing models.
Keywords: electricity spot price; day-ahead market; long-term seasonal component; LASSO; automated variable selection; variance stabilizing transformation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
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Working Paper: Efficient forecasting of electricity spot prices with expert and LASSO models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:8:p:2039-:d:162196
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