A survey of long short term memory and its associated models in sustainable wind energy predictive analytics

Garg, Sherry; Krishnamurthi, Rajalakshmi

doi:10.1007/s10462-023-10554-9

Cited by 4 publications

(3 citation statements)

References 123 publications

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“…The study uses both univariate and multivariate ARIMA models, as well as their recurrent neural network counterparts, to predict wind speed. Recurrent neural network models outperform ARIMA models, whereas multivariate models outperform univariate models, according to the study's findings [24]. Finally, it is possible to infer that the use of machine learning algorithms in wind speed prediction is increasing on a daily basis in order to more precisely predict wind speed while also reducing prediction time and cost.…”

Section: Wind Speed Prediction Using ML Techniquesmentioning

confidence: 75%

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A New ANN Technique for Short-Term Wind Speed Prediction Based on SCADA System Data in Turkey

Reja,

Amin,

Tasneem

et al. 2023

Atmosphere

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The restored interest now receives renewable energy due to the global decline in greenhouse gas emanations and fossil fuel combustion. The fasted growing energy source, wind energy generation, is recognized as a clean energy source that has grown fast and is used extensively in wind power-producing facilities. This study’s short-term wind speed estimations are made using a multivariate model based on an artificial neural network (ANN) that combines several local measurements, including wind speed, wind direction, LV active power, and theoretical power curve. The dataset was received from Turkey’s SCADA system at 10-min intervals, and the actual data validated the expected performance. The research took wind speed into account as an input parameter and created a multivariate model. To perform prediction outcomes on time series data, an algorithm such as an artificial neural network (ANN) is utilized. The experiment verdicts reveal that the ANN algorithm produces reliable predicting results when metrics like 0.693 for MSE, 0.833 for RMSE and 0.96 for R-squared or Co-efficient of determination are considered.

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Section: Wind Speed Prediction Using ML Techniquesmentioning

confidence: 75%

“…The term "short-term wind speed prediction" refers to hourly and daily prediction for the next 24 and 72 h, respectively. In [24], wind speed data were chosen to see how a space-time model might estimate wind speed. Furthermore, wind speed demonstrates significant non-linearity and non-stationarity.…”

Section: Ann-based Short-term Wind Speed Predictionmentioning

confidence: 99%

A New ANN Technique for Short-Term Wind Speed Prediction Based on SCADA System Data in Turkey

Reja,

Amin,

Tasneem

et al. 2023

Atmosphere

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“…Moreover, methods for predicting load using LSTM and genetic algorithms providing a comparison of the ML algorithms are implemented in [41,42], LSTM for univariate household energy forecasting in [43], ensemble learning approach for demand forecasting in [44] and LSTM for predicting wind generation in [45]. Additionally, a survey of LSTM and related models in wind energy predictive analytics was provided [46]. These methods are versatile and can be implemented for various purposes including transaction frauds detection [47], detecting phishing [48], customer satisfaction [49], stock price prediction [50], etc.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

Bâra,

Oprea

2024

Int J Comput Intell Syst

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The energy transition to a cleaner environment has been a concern for many researchers and policy makers, as well as communities and non-governmental organizations. The effects of climate change are evident, temperatures everywhere in the world are getting higher and violent weather phenomena are more frequent, requiring clear and firm pro-environmental measures. Thus, we will discuss the energy transition and the support provided by artificial intelligence (AI) applications to achieve a cleaner and healthier environment. The focus will be on applications driving the energy transition, the significant role of AI, and collective efforts to improve societal interactions and living standards. The price of electricity is included in almost all goods and services and should be affordable for the sustainable development of economies. Therefore, it is important to model, anticipate and understand the trend of electricity markets. The electricity price includes an imbalance component which is the difference between notifications and real-time operation. Ideally it is zero, but in real operation such differences are normal due to load variation, lack of renewable energy sources (RES) accurate prediction, unplanted outages, etc. Therefore, additional energy has to be produced or some generating units are required to reduce generation to balance the power system. Usually, this activity is performed on the balancing market (BM) by the transmission system operator (TSO) that gathers offers from generators to gradually reduce or increase the output. Therefore, the prediction of the imbalance volume along with the prices for deficit and surplus is of paramount importance for producers’ decision makers to create offers on the BM. The main goal is to predict the imbalance volume and minimize the costs that such imbalance may cause. In this chapter, we propose a method to predict the imbalance volume based on the classification of the imbalance sign that is inserted into the dataset for predicting the imbalance volume. The imbalance sign is predicted using several classifiers and the output of the classification is added to the input dataset. The rest of the exogenous variables are shifted to the values from previous day d − 1. Therefore, the input variables are either predicted (like the imbalance sign) or are known from d − 1. Several metrics, such as mean average percentage error (MAPE), determination coefficient R2 and mean average error (MAE) are calculated to assess the proposed method of combining classification machine learning (ML) algorithms and recurrent neural networks (RNN) that memorize variations, namely long short-term memory (LSTM) model.

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Hybrid machine learning model combining of CNN-LSTM-RF for time series forecasting of Solar Power Generation

Abumohsen,

Owda,

Owda

et al. 2024

e-Prime - Advances in Electrical Engineering, Electronics and E

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A survey of long short term memory and its associated models in sustainable wind energy predictive analytics

Cited by 4 publications

References 123 publications

A New ANN Technique for Short-Term Wind Speed Prediction Based on SCADA System Data in Turkey

A New ANN Technique for Short-Term Wind Speed Prediction Based on SCADA System Data in Turkey

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

Hybrid machine learning model combining of CNN-LSTM-RF for time series forecasting of Solar Power Generation

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