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

Abstract

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.

Human Activity Recognition via Hybrid Deep Learning Based Model

“…Mostly a problem having sequential analysis over time such as anomaly recognition [ 37 , 38 ] speech recognition [ 39 , 40 ], person re-identification [ 41 ], Energy forecasting [ 42 , 43 , 44 , 45 ], machine translation [ 46 ], and activity recognition from sensor data [ 47 ] used a special kind of neural network called Recurrent Neural Network (RNN) specifically designed for sequential data analysis having the ability to extract the hidden pattern from sequential data. Generally, the RNN network analyzes the input hidden sequential pattern by concatenating the previous information with current information from both spatial and temporal dimensions and predicting the future sequence [ 48 ]. Although RNN can extract the hidden time-series patterns in sequential data (i.e., sensor, audio, or video data), it is unable to remember/hold long information for a long time and usually fails to deal with the problems having long-term sequences [ 49 , 50 ].…”

A Hybrid Temporal Data Mining Method for Intelligent Train Braking Systems

“…The DB-Net incorporating a dilated convolu-tional neural network (DCNN) with bidirectional long shortterm memory (BiLSTM) to predict power consumption in integrated local energy systems [27]. The AB-Net incorporating an autoencoder (AE) with BiLSTM for Renewable Energy (RE) generation forecasting [28]. A comparative analysis of a variety of deep features with several sequential learning models is presented to select the optimized hybrid architecture for energy consumption prediction [29].…”

Pandemic-Aware Day-Ahead Demand Forecasting Using Ensemble Learning

“…The uncertainties associated with generation and transmission can be addressed through renewable power generation forecasting [2]- [4], and dynamic line rating [5]. On the other hand, demand is also another source of uncertainty in power grids [6].…”