Short-Term Electricity Demand Forecasting Using a Functional State Space Model

Komi Nagbe, Jairo Cugliari and Julien Jacques
Additional contact information
Komi Nagbe: Enercoop, 75019 Paris, France
Jairo Cugliari: ERIC, Université de Lyon, Lyon 2, 69676 Bron Cedex, France
Julien Jacques: ERIC, Université de Lyon, Lyon 2, 69676 Bron Cedex, France

Energies, 2018, vol. 11, issue 5, 1-24

Abstract: In the past several years, the liberalization of the electricity supply, the increase in variability of electric appliances and their use, and the need to respond to the electricity demand in real time has made electricity demand forecasting a challenge. To this challenge, many solutions are being proposed. The electricity demand involves many sources such as economic activities, household need and weather sources. All of these sources make electricity demand forecasting difficult. To forecast the electricity demand, some proposed parametric methods that integrate main variables that are sources of electricity demand. Others proposed a non parametric method such as pattern recognition methods. In this paper, we propose to take only the past electricity consumption information embedded in a functional vector autoregressive state space model to forecast the future electricity demand. The model we proposed aims to be applied at some aggregation level between regional and nation-wide grids. To estimate the parameters of this model, we use likelihood maximization, spline smoothing, functional principal components analysis and Kalman filtering. Through numerical experiments on real datasets, both from supplier Enercoop and from the Transmission System Operator of the French nation-wide grid, we show the appropriateness of the approach.

Keywords: electricity demand forecasting; functional state space model; Kalman filtering; functional data; spline smoothing; functional principal components analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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