28 فروردین 1403

سجاد احمدیان

مرتبه علمی: استادیار
نشانی: دانشگاه صنعتی کرمانشاه
تحصیلات: دکترای تخصصی / مهندسی کامپیوتر
تلفن: 09188339565
دانشکده: دانشکده فناوری اطلاعات

مشخصات پژوهش

عنوان
A Novel Evolutionary-Based Deep Convolutional Neural Network Model for Intelligent Load Forecasting
نوع پژوهش مقاله چاپ شده
کلیدواژه‌ها
Deep convolutional neural networks (CNNs), electricity load forecasting, evolutionary computation, neuroevolution, optimization
پژوهشگران سید محمد جعفر جلالی (نفر اول)، سجاد احمدیان (نفر دوم)، عباس خسروی (نفر سوم)، میعادرضا شفیعی خواه (نفر چهارم)، سعید نهاوندی (نفر پنجم)، ژائو کاتالائو (نفر ششم به بعد)

چکیده

The problem of electricity load forecasting has emerged as an essential topic for power systems and electricity markets seeking to minimize costs. However, this topic has a high level of complexity. Over the past few years, convolutional neural networks (CNNs) have been used to solve several complex deep learning challenges, making substantial progress in some fields and contributing to state of the art performances. Nevertheless, CNN architecture design remains a challenging problem. Moreover, designing an optimal architecture for CNNs leads to improve their performance in the prediction process. This article proposes an effective approach for the electricity load forecasting problem using a deep neuroevolution algorithm to automatically design the CNN structures using a novel modified evolutionary algorithm called enhanced grey wolf optimizer (EGWO). The architecture of CNNs and its hyperparameters are optimized by the novel discrete EGWO algorithm for enhancing its load forecasting accuracy. The proposed method is evaluated on real time data obtained from datasets of Australian Energy Market Operator in the year 2018. The simulation results demonstrated that the proposed method outperforms other compared forecasting algorithms based on different evaluation metrics.