Aurora Energy Research - Wer wir sind
Aurora combines in-depth UK, German and EU policy experience with knowledge of academic research in energy, the environment, and finance.
Our directors and advisors define the company’s research agenda, in consultation with clients, and their expertise shapes Aurora’s forecasting models, analyses, and reports.
Bright economists, engineers, and programmers from the world’s best universities make up Aurora’s team. We think through the economics, and follow the evolution of policy meticulously, undertaking modelling and research to make it easy for our clients to answer the questions that matter for their organisations.
We design our models with a view to providing the most accurate and complete answer given the realities of the marketplace. In developing our models, we deploy insightful techniques, adopt the world’s best practice, and focus on five core modelling principles:
- Proven performance – All Aurora models must be capable of reproducing important historical time series with a high degree of accuracy
- Simplicity – Our models are parsimonious, yet they capture the complexity of the world. We take care to ensure our models are not ‘black boxes’, but transparent and well-documented
- Fit-for-purpose – Energy markets cannot be modelled separately from each other or from the economy as a whole. Our modelling framework incorporates the critical energy market dependencies, providing a consistent simulation of potential market outcomes
- Interconnectivity – Our models are designed to speak to each other. We ensure that they share common notation and programming language. Only integrated modelling approaches produce consistent results
- Theoretical accuracy – We draw heavily on economic theory to complement our extensive model databases. Because energy systems are governed by well-known physical relationships, they often exhibit non-linear relationships consistent with existing theory, which are difficult to identify by mining data. As a consequence, data-only approaches to forecasting energy variables perform poorly over the medium- to long-run, a period over which major shifts often occur. Our approach of combining empirically verified theory with data overcomes this problem