Power System Load Forecasting Based on Grey GM(1,1) Theory

The Grey GM(1,1) model is a mathematical model widely applied in power system load forecasting. This model processes limited and incomplete original data sequences to uncover inherent patterns, enabling future load predictions. Compared with traditional statistical methods, grey forecasting requires less data volume, making it particularly suitable for scenarios with limited historical data that demand rapid forecasting. In power load forecasting applications, the core concept of GM(1,1) involves accumulating generation of original non-negative load data to weaken randomness and enhance trend characteristics. The implementation typically follows these algorithmic steps: First, perform cumulative generation operation (AGO) on raw data to create a monotonic sequence. Then establish a first-order differential equation to describe the pattern of the accumulated sequence. The model parameters are solved using least squares method, followed by inverse accumulation operation to restore future load values. Three critical considerations for practical program implementation: First, verify whether the level ratio of original data falls within the acceptable coverage range to ensure model applicability - this can be implemented through a ratio validation function. Second, prediction accuracy can be improved through residual correction or Markov chain optimization algorithms. Finally, results should be calibrated considering power system characteristics such as seasonal fluctuations and holiday effects. The method's advantage lies in its small computational requirements and ability to handle "small sample, poor information" forecasting needs. However, for long-term predictions, it's essential to incorporate metabolic models to eliminate accumulated errors, which can be programmed through dynamic data window updates.