Abstract
Industrial and building sectors demand efficient smart energy strategies, techniques of optimization, and efficient management for reducing global energy consumption due to the increasing world population. Nowadays, various artificial intelligence (AI) based methods are utilized to perform optimal energy forecasting, different simulation tools, and engineering methods to predict future demand based on historical data. Nevertheless, nonlinear energy demand modeling is still unfledged for a better solution to handle short-term and long-term dependencies and avoid static nature because it is purely on historical data-driven. In this paper, we propose an ensemble deep learning-based approach to predict and forecast energy demand and consumption by using chronological dependencies. Our system initially processes the data, cleaning, normalization, and transformation to ensure the model performance. Furthermore, the preprocess data is fed to proposed ensemble model to extract hybrid discriminative features by using convolution neural network (CNN), stacked, and bi-directional long-short term memory (LSTM) architectures. We trained our proposed system on the historical data to forecast the energy demand and consumption with a different time interval. In the proposed technique, we utilized the concept of active learning based on moving windows to ensure and improve the prediction performance of the system. The proposed system could be applicable to employ energy consumption in industrial and building sectors to demonstrate their significance and effectiveness. We evaluated the proposed system by using benchmark, residential UCI, and local Korean commercial building datasets. We conducted different extensive experimentation to show the error rate and used various kinds of evaluation matrices, which indicate the lower error rate of the proposed system.
Highlights
Population growth, advancement in technologies, and socioeconomics is a greater extent, which risen a demand from the past few decades for the consumption of energy and material
Nowadays artificial intelligence (AI)-based methods are the most popular due to their high-performance outcomes and reliability [9]. These AI-based methods such as convolution neural network (CNN), recurrent neural network (RNN), multi-layer perceptron (MLP), and ensemble methods have been vastly used for time-series and energy forecasting problems [10]
We propose a hybrid deep learning approach to extract high-level spatial cues by CNN layers and handle the non-linear complex behavior, long-term dependencies, and sequences power patterns by stacked and bi-directional long-short term memory (LSTM) networks
Summary
Population growth, advancement in technologies, and socioeconomics is a greater extent, which risen a demand from the past few decades for the consumption of energy and material. Nowadays AI-based methods are the most popular due to their high-performance outcomes and reliability [9] These AI-based methods such as CNN, recurrent neural network (RNN), multi-layer perceptron (MLP), and ensemble methods have been vastly used for time-series and energy forecasting problems [10]. The MLP network provides good outcomes and shows better capability as compared to traditional methods but it is not capable of historical dependencies and long-term sequence handling in time-series data. Due to these issues, the attention of the research community has diverted to CNN and RNN methods. Recently there have been many techniques which were proposed by researchers to handle, predict, and model the long and short-term dependency for energy forecasting and demand at household level and region-wise [12, 13]
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