AleaSoft Energy Forecasting, May 22, 2025. In a context marked by decarbonisation, electrification and market volatility, forecasting methodologies are essential for decision‑making. The combination of statistical, fundamental and artificial intelligence models allows for more accurate forecasting of demand, renewable energy generation and electricity market prices, providing a solid foundation for market operations, investment and risk management.
In a constantly evolving energy sector, forecasts have become a strategic tool. It is not just a matter of anticipating prices or demand, but of making informed decisions in a volatile environment, where the growing share of renewable energy, the electrification of the economy and regulatory developments continually reshape planning models.
Long‑term forecasting is especially critical in the current energy transition context towards decarbonisation and energy independence. Achieving climate neutrality targets and minimising risks in an uncertain environment requires forecasting tools based on a coherent vision of the future energy system and a strong scientific foundation.
Statistical and econometric models: the starting point
Classical forecasting methodologies rely on historical analysis of variables such as electricity demand, renewable energy production, spot and futures prices, or weather patterns. Models such as ARIMA, multivariate regressions or seasonal decomposition capture trends, seasonality and linear relationships between variables, and are particularly useful for short‑ and mid‑term horizons.
Fundamental models: simulating the system’s real operation
Fundamental models go beyond purely statistical approaches by simulating the physical and economic behaviour of the electricity system. Rather than directly forecasting based on historical data, these models replicate the functioning of market agents ‑generators, consumers, operators‑ incorporating supply and demand curves, technical constraints of power plants (e.g. ramping, variable costs, outages), cross‑border interconnections, energy flows and matching and marginalist prices rules.
Their aim is to simulate hourly dispatch and calculate resulting prices, enabling realistic long‑term scenario analysis, such as the entry or exit of technologies, regulatory changes or gas and CO₂ price evolution.
Fundamental models do not depend on historical time series, enabling future scenarios to be modelled with new renewable or energy storage capacity not yet connected, and assessing the impact of political decisions or technological advances. This makes them especially useful for viability studies of projects with 20‑ to 30‑year timeframes.
At AleaSoft Energy Forecasting, fundamental models are integrated with statistical methodology and AI, delivering coherent, justifiable forecasts tailored to the needs of banks, funds and investors.
Artificial intelligence and machine learning: capturing the complexity of the electricity system
In the era of big data and energy sector digitalisation, traditional forecasting methods face limitations in capturing increasing complexity of the system. Here, Artificial Intelligence (AI) and Machine Learning (ML) come into play, enabling the identification of hidden, non‑linear, dynamic patterns within large datasets.
These techniques are particularly valuable in high‑uncertainty contexts, such as intermittent renewable energy generation (especially solar and wind energy), demand under extreme weather conditions or the influence of external events on prices.
Neural networks applied to the electricity market
Artificial neural networks (ANN) are among the most powerful tools in AI‑based energy forecasting. These networks mimic the human brain using layers of nodes (neurons): input, hidden and output. As data passes through these layers, the network learns to adjust internal weights to minimise forecasting error.
Types of neural networks relevant to energy forecasting
In the field of energy forecasting, there are several types of artificial neural networks that are used depending on the data nature and the purpose of the analysis. Feedforward neural networks (FFNN) are the simplest, with information flowing in one direction. They are suitable for demand and market price forecasting with structured data. Recurrent neural networks (RNN), which incorporate “memory” to consider time sequences, are ideal for hourly or daily forecasts of time‑dependent variables. An advanced version, LSTM (Long Short‑Term Memory) networks, overcome the “vanishing gradient” problem and are particularly effective in modelling long, variable time series, such as spot prices or renewable energy production. Finally, convolutional networks (CNN), typically used in computer vision, are increasingly applied to identify spatial and temporal patterns in weather maps and multivariate series.
Specific applications of neural networks in the electricity sector
In the electricity sector, neural networks have very specific applications that significantly improve forecast accuracy and operational efficiency. In solar and wind energy production forecasting, they allow anticipating renewable energy generation with high accuracy from hourly weather data such as irradiance, wind speed or temperature. For electricity demand forecasting, these networks capture consumption patterns throughout the day, week or year, including holidays and weather effects. In market price forecasting, both in the spot and intraday markets, neural networks are capable of integrating multiple variables, such as forecasted demand, expected generation, gas and CO2 prices or import and export flows, to accurately estimate the hourly price of the electricity market. In addition, in optimising the use of batteries, these tools allow predicting the evolution of prices and the state of charge, in order to maximise the profitability of arbitrage or participation in ancillary services.
Advantages of neural networks in energy forecasting
Neural networks offer several advantages for energy forecasting. Their ability to capture complex, non‑linear relationships allows accurately modelling the behaviour of highly interdependent variables, such as demand, renewable energy generation or market prices. In addition, they stand out for their adaptability to real‑time data and their capacity for continuous updating, which makes them particularly effective in dynamic and changing environments. Another of their strengths is their ability to generalise behaviour even in new situations, which makes it possible to incorporate emerging phenomena that are not present in historical data.
At AleaSoft Energy Forecasting, neural networks are part of a hybrid approach, combined with statistical and fundamental models, ensuring robust, explainable forecasts for the different time horizons, from daily operations to 30‑year planning.
The strength of the hybrid approach
The most robust models combine multiple methodologies. AleaSoft Energy Forecasting employs a hybrid approach integrating statistical models, artificial intelligence and fundamental models. This leverages the strengths of each technique, accuracy in the short term, interpretability and realistic simulation in the long term.
Source: AleaSoft Energy Forecasting.The Alea methodology, developed and refined over more than 25 years, exemplifies the advanced hybridisation of statistical, econometric, AI and fundamental models. Its effectiveness has been demonstrated, for instance, by a 2010 forecast that projected the Iberian market’s evolution with remarkable accuracy over more than a decade, despite significant shifts in the generation mix and energy context. This reliability has been key for financial institutions and investors in risk assessment and renewable energy project financing.
Long-term price forecast for the Iberian MIBEL electricity market carried out at the end of October 2010 by AleaSoft.Source: AleaSoft Energy Forecasting.
The critical importance of data
No model is better than the data that feeds it. Data quality, granularity and timeliness are crucial to forecast reliability in the energy sector. This includes appropriate temporal resolution, 15‑minute, hourly, daily, etc., and the implementation of outlier removal and error correction processes. Harmonisation between data sources, especially when combining datasets of different origins, is essential, along with using official and proprietary data to ensure traceability, consistency and added value.
Validation, backtesting and continuous improvement
Reliable forecasting requires rigorous model validation using techniques such as cross‑validation, backtesting against real data and objective metrics like MAE (Mean Absolute Error), RMSE (Root Mean Square Error) o MAPE (Mean Absolute Percentage Error). Moreover, models must be regularly updated to reflect market conditions, new regulations or technological developments.
A key tool for strategic decisions
Over the long term, electricity markets tend towards an equilibrium price determined by the intersection of supply and demand. This equilibrium allows producers to achieve expected returns over asset lifetimes and ensures competitive pricing for consumers. Although prices fluctuate due to short-term factors like weather, gas prices or nuclear outages, the market naturally gravitates towards equilibrium. AleaSoft Energy Forecasting’s hybrid models reflect this stochastic behaviour, realistically simulating market fluctuations around equilibrium.
Sound energy market forecasts support battery and renewable energy investments, PPA contracting, hedging in futures markets, hybridisation planning and auction participation. In short, they turn uncertainty into competitive advantage.
AleaSoft Energy Forecasting’s analysis on the prospects for energy markets in Europe and energy storage
The AleaBlue division of AleaSoft Energy Forecasting provides short- and mid‑term energy market forecasts, essential for planning, energy management, offer generation, risk hedging and decision‑making. Services include electricity demand forecasts and intraday and ancillary services market price forecasts, all critical for optimising arbitrage strategies with energy storage.
Meanwhile, the AleaStorage division provides reports that calculate battery revenue and profitability, size optimal storage systems for hybrid renewable energy projects and deliver tailored analyses for different business models.
Source: AleaSoft Energy Forecasting.
