Engineering

Microeconomics

Economics

Power (physics)

Computer science

Electricity generation

Thermodynamics

Physics

Functional data analysis

Electricity price

Autoregressive model

Electricity demand

Bidding

Nonparametric statistics

Econometrics

Electricity market

Electricity

Machine learning

Electrical engineering

Journal of Forecasting

This work proposes a new approach for the prediction of the electricity price based on forecasting aggregated purchase and sale curves. The basic idea is to model the hourly purchase and the sale curves, to predict them and to find the intersection of the predicted curves in order to obtain the predicted equilibrium market price and volume. Modeling and forecasting of purchase and sale curves is performed by means of functional data analysis methods. More specifically, parametric (FAR) and nonparametric (NPFAR) functional autoregressive models are considered and compared to some benchmarks. An appealing feature of the functional approach is that, unlike other methods, it provides insights into the sale and purchase mechanism connected with the price and demand formation process and can therefore be used for the optimization of bidding strategies. An application to the Italian electricity market (IPEX) is also provided, showing that NPFAR models lead to a statistically significant improvement in the forecasting accuracy.

Forecasting of electricity price through a functional prediction of sale and purchase curves