Abstract
Forecasting the electricity load provides its future trends, consumption patterns and its usage. There is no proper strategy to monitor the energy consumption and generation; and high variation among them. Many strategies are used to overcome this problem. The correct selection of parameter values of a classifier is still an issue. Therefore, an optimization algorithm is applied with deep learning and machine learning techniques to select the optimized values for the classifier’s hyperparameters. In this paper, a novel deep learning-based method is implemented for electricity load forecasting. A three-step model is also implemented, including feature selection using a hybrid feature selector (XGboost and decision tee), redundancy removal using feature extraction technique (Recursive Feature Elimination) and classification/forecasting using improved Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The hyperparameters of ELM are tuned with a meta-heuristic algorithm, i.e., Genetic Algorithm (GA) and hyperparameters of SVM are tuned with the Grid Search Algorithm. The simulation results are shown in graphs and the values are shown in tabular form and they clearly show that our improved methods outperform State Of The Art (SOTA) methods in terms of accuracy and performance. The forecasting accuracy of Extreme Learning Machine based Genetic Algo (ELM-GA) and Support Vector Machine based Grid Search (SVM-GS) is 96.3% and 93.25%, respectively. The accuracy of our improved techniques, i.e., ELM-GA and SVM-GS is 10% and 7%, respectively, higher than the SOTA techniques.
Highlights
Load forecasting has a huge impact on routine electric functions including fuel resource management and for accurate decision making to stabilize the demand and supply of electricity.After revolutionary overhauling of the electricity market internationally, the importance of load forecasting has increased multifold and encompassed other areas of significance, e.g., financialEnergies 2020, 13, 2907; doi:10.3390/en13112907 www.mdpi.com/journal/energiesEnergies 2020, 13, 2907 planning and energy trading, etc
A machine learning and deep learning-based model is proposed, i.e., Extreme Learning Machine based Genetic Algorithm (ELM-GA) and Support Vector Machine based Grid Search (SVM-GS), the hyperparameter values are tuned using an optimization algorithm to obtain maximum accuracy, DT, XGboost and removal using Feature Extraction (RFE) are used in the feature engineering process for removing the redundancy and cleaning the data, the GA and GS optimization algorithms are applied to the ELM and SVM to calculate the optimum hyperparameter values
The daily electricity load data of three years, i.e., January 2017 to December 2019 are used in this paper, which is taken from Independent System Operator New England (ISO NE)
Summary
Load forecasting has a huge impact on routine electric functions including fuel resource management and for accurate decision making to stabilize the demand and supply of electricity. SG brings a revolution by efficiently managing the power generation It distributes the electricity as per the requirement and consumption of the end-users. The main purpose of the power grid is to supply power to end-users It extends from a small local design to meet the daily needs of hundreds or millions of consumers through ultra-long high-voltage and low-voltage lines and makes an excellent interconnect structure. Many techniques are proposed to address these aforementioned issues; there still exist some challenges, i.e., fluctuation in energy generation and consumption to control the fluctuating behaviour between the energy consumption pattern and generation pattern, technique accuracy and tuning the hyperparameters for the prediction of electricity load data To address these issues, a machine learning and deep learning-based model is proposed. A machine learning and deep learning-based model is proposed, i.e., Extreme Learning Machine based Genetic Algorithm (ELM-GA) and Support Vector Machine based Grid Search (SVM-GS), the hyperparameter values are tuned using an optimization algorithm to obtain maximum accuracy, DT, XGboost and RFE are used in the feature engineering process for removing the redundancy and cleaning the data, the GA and GS optimization algorithms are applied to the ELM and SVM to calculate the optimum hyperparameter values
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