Forecasting using Deep learning LSTM network in MATLAB| MATLAB solutions |Deep learning
In this project, our aim is to develop a Neural Network model to forecast power consumption in MWh for upcoming hours or days. We will accomplish this by designing a code that imports a dataset containing historical power consumption data, which will serve as valuable input for prediction. The model will be trained using this data to predict the future values of the desired variables. The key technology employed in this project is the LSTM architecture, which is a type of artificial neural network widely used in deep learning and artificial intelligence. Unlike traditional feedforward neural networks, LSTM networks incorporate feedback connections. This allows them to analyse entire sequences of data, in addition to individual data points. The name "Long Short-Term Memory" arises from the network's ability to retain both long-term and short-term memory. During the training process, each iteration will involve modifying the network's connection weights and biases. By fine-tuning these parameters, the model learns to make accurate predictions based on the historical data. Ultimately, our goal is to create an effective LSTM neural network scheme for power consumption forecasting, enhancing our understanding and prediction capabilities in this domain.
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