| | Nov - Dec 202019Forecasts are an integral part of decision-making in energy trading and risk management. Their importance will only increase as the long-term trends around decarbonisation, digitalisation, and shift towards distributed generation will drive complexity and volatility. A systematic approach that harnesses the power of modern analytics and machine learning to optimise forecast performance is necessary to remain competitive.Forecasts play a crucial role in the energy sector as a key driver of decision-making in trading and risk management. This is also the case for long-term valuation and strategic planning as energy companies are highly exposed to external factors including commodity prices, macroeconomic variables, policy, and regulation. These factors typically explain more than two-thirds of the enterprise value of an energy company.In energy trading, fundamental factors such as weather, flows, and balances as well as technical indicators, volatility, skewness, market positioning, hedging activities of other participants are all in the mix of variables that drive opportunity and risk. Many of these factors are highly variable and uncertain themselves. The complexity is also increasing as renewable and distributed generation technologies are penetrating the market, intensifying generation intermittency and price volatility while shifting value away from larger wholesale markets into multiple smaller and more fragmented pockets. In this environment, predicting future movements, recognising emerging patterns and responding to them rapidly are imperative. This has been driving energy trading organisations to invest in proprietary solutions driven by data science. Thankfully, the availability of granular digital data has also been improving and many vendors now offer specialist data products and forecasts which can be tailored to a user's requirements. However, there are still challenges. One of the issues is about forecast variance. As forecasters multiplied in numbers so did their views and many forecasts tend to differ substantially across vendors. Similarly, forecasts can shift A SYSTEMATIC APPROACH TO FORECASTING IN ENERGY TRADING: GETTING THE MOST OUT OF YOUR PREDICTIONSBy Nazim Osmancik, Group Head of Economics and Fundamentals, Centrica PlcNazim OsmancikCXO INSIGHTS
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