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...

The classical approach to long term load forecasting is often limited to the use of load and weather information occurring with monthly or annual frequency. This low resolution, infrequent data can sometimes lead to inaccurate forecasts. Load forecasters often have a hard time explaining the...

Energy forecasting is one of those areas of great importance to electric grid that gets little attention - even from power industry insiders. But you need to know how to make the best of your forecasting process. Here are 13 tips to get you started.

When turning on the switch, people expect the light would be on. However, the business to keep the lights on is not that straightforward. This paper offers a practical overview of energy forecasting, an important task that electric utilities have been doing every day for over a century.

Electric load forecasting is a fundamental business process and well-established analytical problem in the utility industry. Due to various characteristics of electricity demand series and the business needs, electric load forecasting is a classical textbook example and popular application field...

The Global Energy Forecasting Competition (GEFCom2012) attracted hundreds of participants worldwide, who contributed many novel ideas to the energy forecasting field. This paper introduces both tracks of GEFCom2012, hierarchical load forecasting and wind power forecasting, with details on the...

This empirical paper is a continuation of our earlier work on time series forecasting of day-ahead electricity prices. Given the controversy in the literature whether to use one large model across all hours or 24 separate models, we study if the model structure (and not only the coefficients)...

We investigate the forecasting power of different time series models for electricity spot prices. The models include different specifications of linear autoregressive time series with heteroscedastic noise and/or additional fundamental variables and non-linear regime-switching TAR-type models....

We examine possible accuracy gains from using factor models, quantile regression and forecast averaging for computing interval forecasts of electricity spot prices. We extend the Quantile Regression Averaging (QRA) approach of Nowotarski and Weron (2014) and use principal component analysis to...