Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, a technique that is precise, reliable, and has short-time processing is necessary. This paper uses two methodologies for short-term multinodal load forecasting. The first individually forecasts the local loads and the second forecasts the global load and individually forecasts the load participation factors to estimate the local loads. For the forecasts, a modified general regression neural network and a procedure to automatically reduce the number of inputs of the artificial neural networks are proposed. To design the forecasters, the previous study of the local loads was not necessary, thus reducing the complexity of the multinodal load forecasting. Tests were carried out by using a New Zealand distribution subsystem and the results obtained were found to be compatible with those available in the specialized literature.

This study presents a comparison of two developed intelligent systems that carries out, in an integrated manner, failure diagnosis on electric power distribution feeders. These procedures aim to identify and classify critical situations, as high‐impedance faults, which can potentially damage the system components and cause power supply interruptions to consumers. The intelligent systems combine the wavelet transform, Dempster–Shafer evidence theory, voting scheme, fuzzy inference system and artificial neural networks. Results show the efficiency, reliability, and robustness of the proposed methodology, allowing its real‐time application.