A Wavelet-based hybrid multi-step Wind Speed Forecasting model using LSTM and SVR

Jaseena, K U; Kovoor, Binsu C.

doi:10.1177/0309524x20964762

Cited by 15 publications

(8 citation statements)

References 41 publications

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“…Multiple vector regression analysis (Multi-SVR) is a common kind of time-series forecasting model, which can use statistical methods to determine the quantitative relationship of the interdependence between multiple variables [158][159][160]. Especially the modeling of causal forecasting in the big data, Multi-SVR is used to establish the forecasting model with high accuracy, and mainly used in the model to determine the development regularity of time series data [161][162][163][164]. CNN has the analysis ability of using the convolution kernel feature analysis, especially the hidden layer's awareness of the deep network information [165][166][167][168][169].…”

Section: The Short-term Wind Power Forecasting Based On the Hidden-layers Topology Analysismentioning

confidence: 99%

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

Xu¹,

Shao²,

Deng³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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As wind and photovoltaic energy become more prevalent, the optimization of power systems is becoming increasingly crucial. The current state of research in renewable generation and power forecasting technology, such as wind and photovoltaic power (PV), is described in this paper, with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting. The methods for forecasting wind power and PV production. The physical model, statistical learning method, and machine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production. Moreover, the experiments demonstrated that cloud map identification has a significant impact on PV generation. With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes, this paper summarizes the classification of wind power and PV generation systems, as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.

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Section: The Short-term Wind Power Forecasting Based On the Hidden-layers Topology Analysismentioning

confidence: 99%

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

Xu¹,

Shao²,

Deng³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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“…Accordingly, the integration of DL models, specifically LSTM models with DWT, proved to be powerful tools for modelling energy demand [74], PV energy generation [75], and wind speed [76]. Recently, hybridization of LSTM and Wavelet Decomposition methods proved to be high-performance tools in the prediction of wind power generation [65], wind speed [15], and energy consumption [34].…”

Section: B Discrete Wavelet Decompositionmentioning

confidence: 99%

“…Accordingly, wind and solar energy sources have the highest uncertainty among these groups. One of the goals of this study is to predict the wind and solar energy generation patterns [14] as the integration of DWT and LSTM proved to be a capable method in modelling this type of time series problem [15], [64]. According to climatic factors, we developed LSTM and DWT-LSTM models to predict wind and solar energy supply on an hourly basis.…”

Section: E Energy Supply Modelingmentioning

confidence: 99%

“…However, PV outputs fluctuate due to variation of solar irradiance and temperature [14] which is the main challenge associated with the technology [8]. Wind energy is also another greatest progressing renewable energy resource (RER) with high potentials [15]. Moreover, there are hybrid RERs that combine different energy generation sources such as wind-solar, solar-hydro, and wind-hydro hybrids [16].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

et al. 2021

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Over 33% of final energy consumption is used in buildings which leads to nearly 40% of total direct and indirect CO 2 emissions in the world. While energy consumption is steadily rising globally, managing building energy utilization by on-site renewable energy generation can help responding this demand. This paper proposes a deep learning method based on a discrete wavelet transformation and long short-term memory method (DWT-LSTM) and a scheduling framework for the integrated modelling and management of energy demand and supply for buildings. This method considers several factors including a two-step electricity price, uncertainty in climatic factors, availability of renewable energy resources (wind and solar), energy consumption patterns in buildings, and the non-linear relationships between these parameters. This novel method analyzes and continuously learns from data patterns based on hourly, daily, weekly and monthly intervals. The method enables monitoring and controlling renewable energy generation, the share of energy imports from the grid, employment of saving strategy based on the user priority list, and energy storage management. The main objective of the proposed method is to minimize the reliance on the grid and electricity cost, especially during the peak days. The results demonstrate that the proposed method can forecast building energy demand and energy supply with a high level of accuracy, showing a 3.63-8.57% error range in hourly data prediction for one month ahead. The results also show that the method can supply 304 days (83.2%) of a year without reliance on energy grids, decreasing 87.2% in energy demand on one hand and exporting annually 7777 kWh to the grid on the other hand. In addition, the rescheduling framework decreased the imported electricity cost with the higher electricity tariff by 98 %. The combination of the deep learning forecasting, energy storage, and scheduling algorithm enables reducing annual energy import from the grid from 6709 to 858 kWh (84.3%).

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“…Occurrences of global and local optima problems, leading to the saturated learning process of the machine learning algorithms 14–23,27–34 Increased computational burden of the learning process 37–41 Certain data dissimilarities resulting in delayed convergence 19–23 …”

Section: Introductionmentioning

confidence: 99%

Multi‐step wind speed and wind power forecasting using variational momentum factor and deep learning based intelligent neural network models

Nachimuthu

Banerjee

Karuppaiah

2021

Concurrency and Computation

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Deep learning based novel intelligent neural network models are developed in this research study and employed for performing multi‐step wind speed and wind power forecasting for the data pertaining to certain wind farms. It has always been tedious to predict wind speed and wind power accurately due the existence of non‐linearity in the wind farm data and as well previous traditional and heuristic techniques has their own merits and demerits in performing the prediction process. This research study intends to handle the prevailing non‐linearity of the wind farm data and as well perform prediction of the parameters in a better manner with increased accuracy rate. The prediction study facilitates the renewable energy community to install the wind mills in the locations with higher accuracy rate and thereby power production gets increased extravagantly. The intelligent neural network developed in this article includes the ELMAN and spiking neuronal models with incorporated deep learning procedure and varying momentum factor criterion to achieve minimal error and better accuracy rate. Multi‐step forecasting is carried for 10‐min ahead and the numerical simulation executed with the proposed intelligent non‐linear forecasting techniques. The attained results confirm the superiority of the developed models over other techniques from previous works.

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A Wavelet-based hybrid multi-step Wind Speed Forecasting model using LSTM and SVR

Cited by 15 publications

References 41 publications

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

Multi‐step wind speed and wind power forecasting using variational momentum factor and deep learning based intelligent neural network models

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