Forecasting using Deep learning LSTM network in MATLAB

Introduction

MATLABSolutions demonstrate 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.
Forecasting time series data is a critical yet challenging task in data analysis. The accuracy and effectiveness of forecasting methodologies heavily depend on the nature of the time series data and its contextual factors. Factors such as seasonality, economic shocks, unexpected events, and internal organizational changes can significantly impact the accuracy of forecasts. Long Short-Term Memory (LSTM) networks address the long-term dependency problem encountered in other models. LSTMs excel at learning and retaining information over extended periods without difficulty. Recurrent neural networks (RNNs) generally consist of a series of neural network modules repeated sequentially. In traditional RNNs, these modules have a simple structure, often comprising a single tanh layer. However, various configurations of RNN-based models exist, primarily differing in their ability to retain input data. Standard RNNs often struggle to retain past data, making them inadequate for many tasks. These models follow a feed-forward learning process within the realm of deep learning. In contrast, LSTM networks, a distinct class of RNN models, excel at capturing long-range dependencies between input and output data. These feedback-based models leverage specialized network gates to retain historical information and construct a predictive model based on both past and present data. We will implement a simple LSTM neural network model using MATLAB. Our objective is to forecast data for several hours or days beyond the last available dataset. We will plot the historical and predicted data on the same figure and analyse the accuracy of the fitted data by computing the RMSE or Root mean square error. The following sections will provide a detailed description of the computational prediction process.

Dataset Description

We are provided with a dataset containing 30,000 data points spanning from the year 2019 to 2022. Our goal is to utilize this data to predict future responses. To begin, we normalize the dataset by calculating the mean and standard deviation. This normalization process ensures that the data is on a consistent scale. Next, we proceed with training the LSTM neural network model, which comprises 128 hidden layers and regression layers. This model is designed to forecast the data accurately. Once the model is trained, we can proceed with making predictions

To assess the accuracy of our predictions, we plot the original past data alongside the predicted values on the same figure. This visualization allows for easy comparison and analysis of the forecasted results. Additionally, we compute the Root Mean Square Error (RMSE), which provides a quantitative measure of the accuracy of the predicted outcomes. During the training phase, we will train the model for a maximum of 10,000 epochs. We will employ the ADAM algorithm, a popular optimization algorithm commonly used in deep learning, to update the model's parameters and improve its performance.