Katarzyna Maciejowska

Forecasting of daily electricity spot prices by incorporating intra-day relationships: Evidence form the UK power market

We show that incorporating the intra-day relationships of electricity prices improves the accuracy of forecasts of daily electricity spot prices. We use half-hourly data from the UK power market to model the spot prices directly (via ARX and Vector ARX models) and indirectly (via factor models). The forecasting performance of five econometric models is evaluated and compared with that of a univariate model, which uses only (aggregated) daily data. The results indicate that there are forecast improvements from incorporating the disaggregated data, especially, when the forecast horizon exceeds one week. Additional improvements are achieved when the correlation structure of the intra-day relationships is explored.

Short- and Mid-Term Forecasting of Baseload Electricity Prices in the U.K.: The Impact of Intra-Day Price Relationships and Market Fundamentals

In this paper we investigate whether considering the fine structure of half-hourly electricity prices, the market closing prices of fundamentals (natural gas, coal and CO2) and the system-wide demand can lead to significantly more accurate short- and mid-term forecasts of APX U.K. baseload prices. We evaluate the predictive accuracy of a number of univariate and multivariate time series models over a three-year out-of-sample forecasting period and compare it against that of a benchmark autoregressive model. We find that in the short-term, up to a few business days ahead, a disaggregated model which independently predicts the intra-day prices and then takes their average to yield baseload price forecasts is the best performer. However, in the mid-term, factor models which explore the correlation structure of intra-day prices lead to significantly (as measured by the Diebold-Mariano test) better baseload price forecasts. At the same time, we observe that the inclusion of fundamental variables-especially natural gas prices (in the short-term) and coal prices (in the mid-term)-provides significant gains. The CO2 prices, on the other hand, generally do not improve the price forecasts at all, at least in the time period considered in this study (April 2009-December 2013).

Modeling consumer opinions towards dynamic pricing: An agent-based approach

Using an agent-based modeling approach we show how personal attributes, like conformity or indifference, impact opinions of individual electricity consumers regarding innovative dynamic tariff programs. We also examine the influence of advertising, discomfort of usage and the expectations of financial savings on opinion dynamics. Our main finding is that currently the adoption, understood as a positive opinion or attitude toward the innovation, of dynamic electricity tariffs is virtually impossible due to the high level of indifference in todays societies. However, if in the future the indifference level is reduced, e.g., through educational programs that would make the customers more engaged in the topic, factors like tariff pricing schemes and intensity of advertising will became the focal point.

Fundamental and Speculative Shocks – Structural Analysis of Electricity Market

In the paper, Structural Vector Autoregressive models (SVAR) are used to analyze effects of structural shocks on the electricity prices in UK. The shocks are identified via short run restrictions, which are imposed on the matrix of instantaneous effects. Two main types of shocks are considered: fundamental shocks, identified as demand and wind generation shocks and speculative shocks, which are associated solely with electricity prices. The results indicate that speculative shocks play an important role in the price setting process and account for more than 90 % of the unexpected electricity price variability. Moreover, wind generation shocks have larger input to the electricity price variance than demand shocks, particularly when peak hours are considered.

Principle component forecast averaging — Application to the UK electricity market

In the paper, a forecast combination via dimension reduction techniques is applied to forecasting electricity spot prices. We propose to use a Factor Averaging (FA) method, which is based on a principle component approach. This methodology allows to extract information from a large panel of forecasts and helps to solve two important issues: collinearity of forecasts and common bias of different predicting methods. The empirical performance of FA and other forecast averaging methods is evaluated using the data describing the UK market. Twenty four individual methods are used to calculate one day ahead forecast of 48 hal-hourly prices. The outcomes prove that FA is more efficient in terms of the RMSE than other forecast combining approaches. Gains from using FA varies between 8-20% when comparing the average daily RMSE and between 1-40% when individual half-hourly periods are considered.