NEURAL NETWORK-BASED MODEL DESIGN FOR SHORT-TERM LOAD FORECAST IN DISTRIBUTION SYSTEMS
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The document discusses the design of neural network-based models for accurate short-term load forecasting in electrical distribution systems. It emphasizes the importance of variable and model selection to enhance predictive capabilities, using real data from French substations for validation. The findings indicate that these models outperform traditional time series approaches in terms of generalization capacity for load forecasting.
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NEURAL NETWORK-BASED MODEL DESIGN FOR SHORT-TERM LOAD FORECAST IN DISTRIBUTION SYSTEMS
- 1. NEURAL NETWORK-BASED MODEL DESIGN FOR SHORT-TERM LOAD FORECAST IN DISTRIBUTION SYSTEMS ABSTRACT Accurate forecasts of electrical substations are mandatory for the efficiency of the Advanced Distribution Automation functions in distribution systems. The paper describes the design of a class of machine-learning models, namely neural networks, for the load forecasts of medium-voltage/low-voltage substations.We focus on the methodology of neural networkmodel design in order to obtain a model that has the best achievable predictive ability given the available data. Variable selection and model selection are applied to electrical load forecasts to ensure an optimal generalization capacity of the neural network model. Real measurements collected in French distribution systems are used to validate our study. The results show that the neural network-based models outperform the time series models and that the design methodology guarantees the best generalization ability of the neural network model for the load forecasting purpose based on the same data.