Power load forecasting is a fundamental task for the operation of the power system: how to arrange power generation plans, how much reserve capacity to keep, and how to consume new energy all rely on the judgment of future electricity demand. In the past few decades, this field has undergone three generations of methodological evolution, and each evolution is essentially an upgrade to the "data utilization capability".
The first generation was statistical methods. Represented by time series analysis, models such as autoregression and moving average are established based on historical load data to capture the cyclical patterns of electricity consumption. This type of method is simple and interpretable, but when faced with complex factors such as sudden temperature changes, holidays, and emergencies, the prediction accuracy is significantly insufficient.
The second generation is the era of machine learning. With the popularization of smart meters, the power grid has accumulated a massive amount of fine-grained data, and algorithms such as random forest and gradient boosting have begun to emerge. They can automatically combine features such as temperature, humidity, calendar, and industry electricity consumption, significantly improving short-term forecasting accuracy, but also rely on manual feature engineering, resulting in high model optimization costs.
The third generation is deep learning methods. LSTM, Transformer and other sequence models can directly learn long-range dependencies from historical load sequences, combined with meteorological forecast data as multivariate inputs, and perform more stably in medium - and long-term forecasting and extreme weather scenarios. In recent years, the rise of big models has brought new imagination: translating time-series data into text sequences, using the pattern recognition ability of big models for prediction, or using big models as auxiliary decision assistants for dispatchers to interpret prediction results and generate scheduling suggestions.
The real difficulty in the implementation of AI in the field of electricity often lies not in the model itself, but in the engineering side: data quality governance, confidence intervals for prediction results, integration with existing scheduling systems, and interpretability of the model - dispatchers need to know "why predict tomorrow's peak at 3 pm" instead of just receiving a number.
For practitioners, a pragmatic approach is to first take inventory of existing data and business scenarios, starting from clear input-output scenarios such as short-term load forecasting, establishing a "prediction verification feedback" loop, and gradually expanding to more complex scenarios such as new energy output forecasting and demand response. AI will not replace the experience of dispatchers, but it can build experience on more accurate quantitative judgments.