Short-Term Wind Power Forecasting Using Artificial Neural Networks for Resource Scheduling in Microgrids

Tesfaye, Abinet; Zhang, J. H.; Zheng, Dehua; Shiferaw, Dereje

doi:10.7753/ijsea0503.1005

Cited by 27 publications

(5 citation statements)

References 31 publications

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“…However, in recent literature, research has moved towards the implementation of machine learning (ML) methods, mainly due to the performance of such models in extracting robust features, which have reported huge success in a wide range of applications [10,11]. ML models, such as artificial neural networks (ANN) [12] and support vector machines (SVM) [13], can establish a nonlinear mapping to accurately describe wind randomness. In this line of thought, in [14], wind speed and wind power output are predicted in the short term by means of a group method of data handling (GMDH)-neural network approach.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks

2021

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Wind energy has been recognized as the most promising and economical renewable energy source, attracting increasing attention in recent years. However, considering the variability and uncertainty of wind energy, accurate forecasting is crucial to propel high levels of wind energy penetration within electricity markets. In this paper, a comparative framework is proposed where a suite of long short-term memory (LSTM) recurrent neural networks (RNN) models, inclusive of standard, bidirectional, stacked, convolutional, and autoencoder architectures, are implemented to address the existing gaps and limitations of reported wind power forecasting methodologies. These integrated networks are implemented through an iterative process of varying hyperparameters to better assess their effect, and the overall performance of each architecture, when tackling one-hour to three-hours ahead wind power forecasting. The corresponding validation is carried out through hourly wind power data from the Spanish electricity market, collected between 2014 and 2020. The proposed comparative error analysis shows that, overall, the models tend to showcase low error variability and better performance when the networks are able to learn in weekly sequences. The model with the best performance in forecasting one-hour ahead wind power is the stacked LSTM, implemented with weekly learning input sequences, with an average MAPE improvement of roughly 6, 7, and 49%, when compared to standard, bidirectional, and convolutional LSTM models, respectively. In the case of two to three-hours ahead forecasting, the model with the best overall performance is the bidirectional LSTM implemented with weekly learning input sequences, showcasing an average improved MAPE performance from 2 to 23% when compared to the other LSTM architectures implemented.

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Section: Introductionmentioning

confidence: 99%

Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks

2021

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“…As a result, artificial intelligence (AI)-based forecasting techniques are becoming increasingly popular for short-and long-term generation and load forecasting with low error margins. Researchers have investigated different AI-based techniques, such as ANN, deep neural networks (DNN), and ML approaches, for short-term generation (solar and wind) and load forecasting in the microgrid paradigm [178][179][180][181][182][183][185][186][187]220]. The authors in [188,189] proposed an MAS and binary genetic algorithm (BGA)-based short-term forecasting load and generation model for MG. From long-term forecasting perspectives, [184] proposed a deep learning model for solar radiation forecasting for installation of MGs.…”

Section: Generation and Load Forecastingmentioning

confidence: 99%

Smart Distribution Mechanisms—Part I: From the Perspectives of Planning

et al. 2022

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To enhance the reliability and resilience of power systems and achieve reliable delivery of power to end users, smart distribution networks (SDNs) play a vital role. The conventional distribution network is transforming into an active one by incorporating a higher degree of automation. Replacing the traditional absence of manual actions, energy delivery is becoming increasingly dependent on intelligent active system management. As an emerging grid modernization concept, the smart grid addresses a wide range of economic and environmental concerns, especially by integrating a wide range of active technologies at distribution level. At the same time, these active technologies are causing a slew of technological problems in terms of power quality and stability. The development of such strategies and approaches that can improve SDN infrastructure in terms of planning, operation, and control has always been essential. As a result, a substantial number of studies have been conducted in these areas over the last 10–15 years. The current literature lacks a combined systematic analysis of the planning, operation, and control of SDN technologies. This paper conducts a systematic survey of the state-of-the-art advancements in SDN planning, operation, and control over the last 10 years. The reviewed literature is structured so that each SDN technology is discussed sequentially from the viewpoints of planning, operation, and then control. A comprehensive analysis of practical SND concepts across the globe is also presented in later sections. The key constraints and future research opportunities in the existing literature are discussed in the final part. This review specifically assists readers in comprehending current trends in SDN planning, operation, and control, as well as identifying the need for further research to contribute to the field.

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“…As can be seen in Figure 2, the feed-forward ANN contains two hidden layers, and each hidden layer contains n hidden neurons. The relationship between the inputs and output of each node is defined as (Tesfaye et al, 2016):…”

Section: Artificial Neural Network Based Spatial Correlation Artificial Neural Networkmentioning

confidence: 99%

Short-Term Wind Power Forecasting Using Mixed Input Feature-Based Cascade-connected Artificial Neural Networks

Chen

Folly

2021

Front. Energy Res.

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Accurate short-term wind power forecasting is crucial for the efficient operation of power systems with high wind power penetration. Many forecasting approaches have been developed in the past to forecast short-term wind power. Artificial neural network-based approaches (ANNs) have become one of the most effective and popular short-term wind speed and wind power forecasting approaches in recent years. However, most researchers have used only historical data from a specific station to train the ANNs without considering meteorological variables from many neighboring stations on the forecasting performance. Using additional meteorological variables from neighboring stations can contribute valuable surrounding information to the forecasting model of the target station and improve ANNs performance. In this paper, a mixed input features-based cascade-connected artificial neural network (MIF-CANN) is used to train input features from many neighboring stations without encountering overfitting issues caused by many input features. Multiple ANNs train different combinations of input features in the first layer of the MIF-CANN model to produce preliminary results, then cascading into the second phase of the MIF-CANN model as inputs. The performance of the proposed MIF-CANN model is compared with the ANNs-based spatial correlation models. Simulation results show that the proposed MIF-CANN has better performance than the ANNs-based spatial correlation models.

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Short-Term Wind Power Forecasting Using Artificial Neural Networks for Resource Scheduling in Microgrids

Cited by 27 publications

References 31 publications

Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks

Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks

Smart Distribution Mechanisms—Part I: From the Perspectives of Planning

Short-Term Wind Power Forecasting Using Mixed Input Feature-Based Cascade-connected Artificial Neural Networks

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