An Improved Model for Electricity Load and Price Prediction Using Hybrid Deep Learning Algorithms: A Comprehensive Review

Dahiru Dalhatu Gital, Abdulsalam Ya'u Gital, Badamasi Imam Ya'u, Abuzairu Ahmed, Mustapha Abdulrahman Lawal

Abstract


Consumption of electricity is increasing in the last decade as urban areas are constantly increasing respectively. There is a growing need to bridge the gap between electricity demand and supplies which can be achieved by improving the forecast accuracy for enhanced planning. A number of techniques and computational approaches have been implored recently in order to improve prediction accuracy. However, most of the studies focus only on load forecasting with very few studies attempting to address both the load and price prediction concurrently. In order to tackle this issue, a hybrid algorithm base on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) algorithm is proposed, which combines the advantages of (CNNs) and (LSTMs). Since CNN’s feature extraction is adaptive and self-learning, the model can overcome the reliance of feature extraction and data reconstruction relying on human experience and subjective consciousness in traditional prediction algorithms (T. Liu et al., 2018).The model uses multiple convolutional kernels to scan the entire dataset to obtain relevant features and discard redundant features. Hence, our goal is to improve the prediction accuracy using a convolutional neural network (CNN) and LSTM and evaluate the model against state-of-the-art models. The research will use MATLAB as the simulation tool; and RMSE, MAE, and R2 as the evaluation metrics. The results will be compared with the existing baseline method. 


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