Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction

Haq

et al. 2021

Sensors

Nowadays, for efficient energy management, local demand-supply matching in power grid is emerging research domain. However, energy demand is increasing day by day in many countries due to rapid growth of the population and most of their work being reliant on electronic devices. This problem has highlighted the significance of effectively matching power demand with supply for optimal energy management. To resolve this issue, we present an intelligent deep learning framework that integrates Atrous Convolutional Layers (ACL) with Residual Gated Recurrent Units (RGRU) to establish balance between the demand and supply. Moreover, it accurately predicts short-term energy and delivers a systematic method of communication between consumers and energy distributors as well. To cope with the varying nature of electricity data, first data acquisition step is performed where data are collected from various sources such as smart meters and solar plants. In the second step a pre-processing method is applied on raw data to normalize and clean the data. Next, the refined data are passed to ACL for spatial feature extraction. Finally, a sequential learning model RGRU is used that learns from complicated patterns for the final output. The proposed model obtains the smallest values of Mean Square Error (MSE) including 0.1753, 0.0001, 0.0177 over IHEPC, KCB, and Solar datasets, respectively, which manifests better performance as compared to existing approaches.

Section: Comparative Analysis and Discussionmentioning

confidence: 99%

Atrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply

Haq

et al. 2021

Sensors

“…Both the solar and wind datasets were considered for the comparative study. The comparison was performed with the most recent method [67], In this section, we compare the proposed method with recent research carried out for power generation forecasting. Both the solar and wind datasets were considered for the comparative study.…”

Section: Assessment With State Of the Artmentioning

confidence: 99%

“…Both the solar and wind datasets were considered for the comparative study. The comparison was performed with the most recent method [67], where a mode-adaptive ANN algorithm is proposed via Spearman's ranking order and population-based algorithms. They evaluate different models such as advanced particle swarm optimization (APSO) and the fine-tuning metaheuristic algorithm (FTMA).…”

Section: Assessment With State Of the Artmentioning

confidence: 99%

AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting

et al. 2021

Renewable energy (RE) power plants are deployed globally because the renewable energy sources (RESs) are sustainable, clean, and environmentally friendly. However, the demand for power increases on a daily basis due to population growth, technology, marketing, and the number of installed industries. This challenge has raised a critical issue of how to intelligently match the power generation with the consumption for efficient energy management. To handle this issue, we propose a novel architecture called ‘AB-Net’: a one-step forecast of RE generation for short-term horizons by incorporating an autoencoder (AE) with bidirectional long short-term memory (BiLSTM). Firstly, the data acquisition step is applied, where the data are acquired from various RESs such as wind and solar. The second step performs deep preprocessing of the acquired data via several de-noising and cleansing filters to clean the data and normalize them prior to actual processing. Thirdly, an AE is employed to extract the discriminative features from the cleaned data sequence through its encoder part. BiLSTM is used to learn these features to provide a final forecast of power generation. The proposed AB-Net was evaluated using two publicly available benchmark datasets where the proposed method obtains state-of-the-art results in terms of the error metrics.

“…The output of the proposed ANN controller is the corresponding torque ( C em *) (Gowri and Popuri, 2020). The most appropriate number of hidden layers and their neurons is decided based on an empirical basis in order to attain the required precision of the proposed approach (Donadio et al, 2021; Zamee and Won, 2020). After reiterating the learning algorithms several times.…”

Section: Introductionmentioning

confidence: 99%

Overview of control strategies for wind turbines: ANNC, FLC, SMC, BSC, and PI controllers

Yessef¹,

Bossoufi²,

Taoussi³

et al. 2022

Wind Engineering

The design of robust and precise control of obtaining maximum power yield is an important area of research in wind engineering. In the context of maximizing the amount of power extraction in wind energy conversion systems (WECS), this research work proposes and evaluates five MPPT algorithms. These types are respectively a proportional integral controller (PI), a non-linear control based on sliding modes (SMC), a backstepping approach (BSC), a control using artificial intelligence based on neural network (ANNC), and a fuzzy logic control (FLC). Two different wind profiles, a step wind profile and a real wind profile, were considered for the comparative study. The response time, dynamic error percentage, and static error percentage were the quantitative parameters compared, and the qualitative parameters included set-point tracking and precision. This test demonstrated the superiority of the ANNC controller with an error static that not exceed 0.39% and a response time ~0.0024 seconds.