Forecasting Electricity Prices

Abstract

Forecasting electricity prices is a challenging task and an active area of research since the 1990s and the deregulation of the traditionally monopolistic and government-controlled power sectors. It is interdisciplinary by nature. It requires expertise in econometrics, statistics or machine learning for developing well-performing predictive models, in finance for understanding market mechanics, and in electrical engineering for comprehension of the fundamentals driving electricity prices.Although electricity price forecasting aims at predicting both spot and forward prices, the vast majority of research is focused on short-term horizons which exhibit dynamics unlike in any other market. The reason is that power system stability calls for a constant balance between production and consumption, while being weather (both demand and supply) and business activity (demand only) dependent. The recent market innovations do not help in this respect. The rapid expansion of intermittent renewable energy sources is not offset by the costly increase of electricity storage capacities and modernization of the grid infrastructure.On the methodological side, this leads to three visible trends in electricity price forecasting research as of 2022. Firstly, there is a slow, but more noticeable with every year, tendency to consider not only point but also probabilistic (interval, density) or even path (also called ensemble) forecasts. Secondly, there is a clear shift from the relatively parsimonious econometric (or statistical) models towards more complex and harder to comprehend, but more versatile and eventually more accurate statistical/machine learning approaches. Thirdly, statistical error measures are nowadays regarded as only the first evaluation step. Since they may not necessarily reflect the economic value of reducing prediction errors, more and more often, they are complemented by case studies comparing profits from scheduling or trading strategies based on price forecasts obtained from different models.

Electricity price forecast in wholesale markets using conformal prediction: Case study in Mexico

“…The theory of ICA is detailed explained in Maciejowska et al 8 Here, we summarize for our case of multiple time series (features) influencing on one time series (target). Consider two time series TS 1 and TS 2 that can be represented as:…”

“…The first scheme is the pool-type where trading, dispatch, and transmission are managed by the system operator whereas the second scheme is a power exchange type where trading and initial dispatch are managed by an institution independent from transmission system operator. 8 Concerning the electricity price bidding the day-ahead (DA) and the real time (RT) markets are usually employed. In both bidding schemes, the electricity price is calculated using algorithms where the forecasted demand is satisfied by considering the supply biddings from market participants.…”

A feature selection method based on Shapley values robust for concept shift in regression

“…The literature related to electricity price forecasting is extensive, with day-ahead (12 to 36 h) forecasting prior to the Day-Ahead Market being the most studied case from a variety of perspectives [28][29][30]. However, the electricity market is constantly subject to regulatory changes and is highly influenced by political and economic situations.…”