Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid

Abstract

Medium-term electricity consumption and load forecasting in smart grids is an attractive topic of study, especially using innovative data analysis approaches for future energy consumption trends. Loss of electricity during generation and use is also a problem to be addressed. Both consumers and utilities can benefit from a predictive study of electricity demand and pricing. In this study, we used a new machine learning approach called AdaBoost to identify key features from an ISO-NE dataset that includes daily consumption data over eight years. Moreover, the DT classifier and RF are widely used to extract the best features from the dataset. Moreover, we predicted the electricity load and price using machine learning techniques including support vector machine (SVM) and deep learning techniques such as a convolutional neural network (CNN). Coronavirus herd immunity optimization (CHIO), a novel optimization approach, was used to modify the hyperparameters to increase efficiency, and it used classifiers to improve the performance of our classifier. By adding additional layers to the CNN and fine-tuning its parameters, the probability of overfitting the classifier was reduced. For method validation, we compared our proposed models with several benchmarks. MAE, MAPE, MSE, RMSE, the f1 score, recall, precision, and accuracy were the measures used for performance evaluation. Moreover, seven different forms of statistical analysis were given to show why our proposed approaches are preferable. The proposed CNN-CHIO and SVM techniques had the lowest MAPE error rates of 6% and 8%, respectively, and the highest accuracy rates of 95% and 92%, respectively.

Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

“…In this article, the history of energy load and price information from several different new England cities is utilized. ISONE, which is utilized in this article [5], maintains the power load information. The vertical data, or columns, are referred to as "features."…”

“…There is a substantial amount of accurate data [4]. In the end-user, a good decision process minimizes power loss, lowers energy expenditures and lowers Peak Average Ratio (PAR) [5].…”

Optimized hierarchical radial basis function neural networks by developing coronavirus herd immunity optimizer for solid oxide fuel cells

“…For example, the original version of this algorithm is used by Aslam et al for electric price and load forecasting using CNN-based ensembler in smart grid. 32 They employed CHI algorithm to develop the efficacy of the classifier hyperparameters to modify the performance of them. Moreover, another work by Makhadmeh et al 33 modifies the CHI algorithm to deal with discrete optimization problems.…”

Multi-Step-Ahead Electricity Price Forecasting Based on Temporal Graph Convolutional Network

“…For instance, Li et al [15] forecasted electricity price based on a long shortterm memory (LSTM) neural network, using a test period of 4 weeks. Aslam et al [16] focused on the performance of a convolutional network (CNN) in medium-term electricity price forecasting, and showed that the CNN model performs well. Yang et al [17] built an innovative model based on a deep neural network (DNN) for electricity price forecasting, using a test dataset spanning a month.…”