Integrating Machine Learning and Stochastic Pattern Analysis for the Forecasting of Time-Series Data

Khan, A.B. Feroz; Kamalakannan, K.; Ahmed, Nadeem

doi:10.1007/s42979-023-01981-0

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…WSN is used in attention mechanism to focus the target area while ignore the irrelevant area. LSTM is suitable for time series data and CNN can be adopted to support LSTM [14,15]. The input of time series data can be referred as it and the units of hidden layer is referred as h and Ht is referred as time series data output.…”

Section: Predictionmentioning

confidence: 99%

Deep Learning-Based Auto-LSTM Approach for Renewable Energy Forecasting: A Hybrid Network Model

Venkatraman,

Pitchaipillai

2024

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In recent years, renewable energy forecasting has gained increasing attention due to its potential to minimize energy resource usage and maximize the security of power plant operation. Deep learning models have emerged as a promising tool for renewable energy prediction. However, the application of these techniques for renewable energy forecasting remains sparse. In this work, we introduce a deep belief network-based auto-LSTM approach that utilizes a wireless sensor network (WSN) for energy forecasting in solar power plants. We also compare this approach with other deep learning techniques, including long short-term memory (LSTM) and artificial neural networks, as well as traditional models such as Multilayer Perception and physical prediction models. We evaluate the performance of these methods using data from 26 solar plants. Our experimental analysis shows that the proposed auto-LSTM-based deep learning approach outperforms the other models in terms of prediction accuracy, demonstrating its efficiency and effectiveness for renewable energy forecasting in solar power plants. By introducing the inventive auto-LSTM model and assessing its effectiveness in comparison to diverse established techniques, our study not only adds to the current discussions on predictive modeling but also tackles the tangible challenges linked with the integration of renewable energy.

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Section: Predictionmentioning

confidence: 99%

Deep Learning-Based Auto-LSTM Approach for Renewable Energy Forecasting: A Hybrid Network Model

Venkatraman,

Pitchaipillai

2024

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“…Exponential smoothing is a widely used technique in time series forecasting that aims to eliminate noise and capture underlying patterns in data [19]. It achieves this by assigning weights to previous observations, with higher weights given to more recent data points.…”

Section: B Exponential Smoothing With Optimum αmentioning

confidence: 99%

Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting

Wibawa,

Utama,

Akbari

et al. 2023

Knowl. Eng. Data Sci.

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Energy use is an essential aspect of many human activities, from individual to industrial scale. However, increasing global energy demand and the challenges posed by environmental change make understanding energy use patterns crucial. Accurate predictions of future energy consumption can greatly influence decision-making, supply-demand stability and energy efficiency. Energy use data often exhibits time-series patterns, which creates complexity in forecasting. To address this complexity, this research utilizes Deep Learning (DL), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) models. The main objective is to improve the accuracy of energy usage forecasting by optimizing the alpha value in exponential smoothing, thereby improving forecasting accuracy. The results showed that all DL methods experienced improved accuracy when using optimum alpha. LSTM has the most optimal MAPE, RMSE, and R2 values compared to other methods. This research promotes energy management, decision-making, and efficiency by providing an innovative framework for accurate forecasting of energy use, thus contributing to a sustainable and efficient energy system.

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