Energy Price Forecasts

Typically, for example, an energy company that operates power plants and trades energy products wants to improve its decision-making process related to energy production and trading. The company wants to optimize its operations, effectively manage risks, and take advantage of market opportunities by using accurate energy price forecasts. As an energy price forecasting provider, we specialize in analyzing historical energy market data and utilizing advanced forecasting techniques. The service includes the following steps:

• Data Collection and Integration: We obtain historical energy market data from various sources, including spot prices, futures contracts, supply & demand data, and weather patterns, to be processed to leverage our forecasts.

• Feature Engineering: We identify relevant variables affecting energy prices, e.g. fuel costs, weather conditions, electricity demand, renewable energy production, and economic indicators.

• Model selection and training: Advanced machine learning algorithms such as time series models, regression models, or more sophisticated techniques such as neural networks are trained on the historical data. The models learn patterns, seasonality, and dependencies between the variables.

• Creation of price forecasts: After training, the models create energy price forecasts for different time horizons. These forecasts provide estimates of future energy prices based on the input signals.

• Validation: The forecast prices are compared to actual market prices to assess the accuracy of the model. The models are further refined and validated using various metrics to ensure reliable and accurate predictions.

• Continuous update: The forecast models are updated regularly as new energy market data becomes available. As market conditions change, the models adapt to capture changing trends and new patterns.