Short Term Electric Load Forecast with Artificial Neural Networks
This work addresses effective energy consumption management in an open market environment, but it is incremental as it applies existing neural network methods to a specific dataset.
The paper tackled short-term electric load forecasting using feedforward and Elman recurrent neural networks, testing 35 different structures on measured data from the Banat area and selecting the best solutions through multiple trainings.
This paper presents issues regarding short term electric load forecasting using feedforward and Elman recurrent neural networks. The study cases were developed using measured data representing electrical energy consume from Banat area. There were considered 35 different types of structure for both feedforward and recurrent network cases. For each type of neural network structure were performed many trainings and best solution was selected. The issue of forecasting the load on short term is essential in the effective energetic consume management in an open market environment.