In continuously evolving power markets, with more renewable generation and changing load patterns, electricity prices become volatile, and market participants must manage their price risk in addition to their volumes and other market uncertainties. Price information from future contracts typically do not provide the flexibility or granularity required to manage risk for specific intervals, such as hours of the day or weekdays. With electricity consumption differing by time of day, day of week, season of year, and being affected by weather conditions, this may lead to unanticipated price changes. Having access to accurate price expectations is therefore imperative and a highly accurate price forecast can manage risk and save market participants money.
The emergence of less-predictable renewable electricity generation has made the dynamics of electricity prices more uncertain and increased volatility in power markets, where positions may change rapidly. In addition, less liquid markets may not provide enough pricing information. As a result, developing more accurate price modelling and forecasting techniques is a challenge for all market participants. Portfolio Managers, who can forecast wholesale power prices, position themselves to adjust their trading strategy or their generation and demand plans, thereby better managing price risk.
Where modern ETRM systems already provide real-time position information, risk scenario capabilities, and price curve management, these functions can be further leveraged when the system is equipped with and integrated ability to forecast short and long-term power prices.
Short term here is defined as next day to a few weeks out, whereas Long-term forecasting can be a month to several years out. As ingredients, the price forecasting solution will use historical price, weather, demand, and generation data, as well as forecasted data. Other input consists of calendar attributes, such as on-peak, off-peak hours, weekdays, weekend days, holidays etc. The inputs are used to train and test multiple machine learning algorithms to balance user needs for accuracy, speed, and interpretability.
Using these algorithms, a forecasting solution can train specifically for the desired time horizon applying feature creation, input reduction, normalization, auto-regression, and other techniques to generate the highest quality price forecast needed for the preferred time horizon. The end results are a trusted price forecast with stochastic bands of potential risk, which enables users to make better decisions, faster.
Whether a utility supporting industrial customers needs to minimize the risk of highly volatile power prices, or a generator must cover their production cost, they are interested in hedging price risk. Outlook begins with insight, and forecasting helps overcome the challenges in the shift towards renewables. A price forward curve generated by a price forecasting solution allows them to:
- Get accurate predictions of future prices to help manage risk and save cost.
- Optimize bidding strategies.
- Identify the best energy markets for a generation profile.
With the advancement of renewable energy into the energy mix, power production, planning and trading becomes more challenging and volatile. The demand for precise and reliable power price forecasting is crucial for making profitable decisions.
An Artificial Intelligence (AI) powered forecasting solution that is accurate, algorithm agnostic, and scalable delivers price forecasts with confidence levels that a trader needs to operate increasingly dynamic and complex markets to guide strategies, take advantage of market arbitrage opportunities, thereby improving revenues.
Forecasting electricity prices is essential information for participants in the wholesale power market, but a challenging task that requires solutions from a partner with domain and data science expertise.