This work discusses potential pitfalls of applying linear regression models to explaining the relationship between spot and futures prices in electricity markets. We briefly introduce the theory for the analysis of the spot-futures price relationship and highlight selected issues of multiple...

A simple Ising spin model which can describe a mechanism of making a decision in a closed community is proposed. It is shown via standard Monte Carlo simulations that very simple rules lead to rather complicated dynamics and to a power law in the decision time distribution. It is found that a...

In 2000 we proposed a sociophysics model of opinion formation, which was based on trade union maxim "United we Stand, Divided we Fall" (USDF) and latter due to Dietrich Stauffer became known as the Sznajd model (SM). The main difference between SM compared to voter or Ising-type models is that...

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...

We evaluate a recently proposed method for constructing prediction intervals, which utilizes the concept of quantile regression (QR) and a pool of point forecasts of different time series models.We find that in terms of interval forecasting of Nord Pool day-ahead prices the new QR-based approach...

In the paper, Structural Vector Autoregressive models (SVAR) are used to identify fundamental and speculative shocks, in the UK electricity market. The structural shocks are identified via short run restrictions, which are imposed on the matrix of instantaneous effects. In the research, two main...

We examine the impact of explanatory variables such as load, weather and capacity constraints on the occurrence and magnitude of price spikes in regional Australian electricity markets. We apply the so-called Heckman correction, a two-stage estimation procedure that allows us to investigate the...

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)....

When building stochastic models for electricity spot prices the problem of uttermost importance is the estimation and consequent forecasting of a component to deal with trends and seasonality in the data. While the short-term seasonal components (daily, weekly) are more regular and less...

We present the results of an extensive study on modeling and forecasting of the long-term seasonal component (LTSC) of electricity spot prices. We consider a vast array of models including linear regressions, monthly dummies, sinusoidal decompositions and wavelet smoothers. We find that in terms...