Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach

Cheng, Lilin; Zang, Haixiang; Ding, Tao; Sun, Rong; Wang, Miaomiao; Wei, Zhinong; Sun, Guoqiang

doi:10.3390/en11081958

Cited by 75 publications

(40 citation statements)

References 58 publications

(30 reference statements)

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“…et al, proposed an ensemble method for probabilistic wind forecasting. The authors used recurrent neural network with different architecture (LSTM, GRU and Dropout layer) for ensemble models, and adaptive neuro fuzzy inference system which were used for combination of final prediction [71]. An adaptive boosting (AdaBoost) combined with Extreme Learning Machine (ELM) for multi-step wind speed forecasting proposed in [72].…”

Section: Ensemble Learning Applicationsmentioning

confidence: 99%

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

Surakhi¹,

Serhan²,

Salah³

2020

Adv. sci. technol. eng. syst. j.

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Time-series is a sequence of observations that are taken sequentially over time. Modelling a system that generates a future value from past observations is considered as time-series forecasting system. Recurrent neural network is a machine learning method that is widely used in the prediction of future values. Due to variant improvements on recurrent neural networks, choosing of the best model for better prediction generation is dependent on problem domain and model design characteristics. Ensemble forecasting is more accurate than single model due to the combination of more than one model for forecasting. Designing an ensemble model of recurrent neural network for time-series forecasting applications would enhance prediction accuracy and improve performance. This paper highlights some of the challenges that are faced by the design of the ensemble model of different recurrent neural network versions, and surveys some of the most relevant works in order to give a direction of how to conduct ensemble learning research in the future. Based on the reviewed literature, we propose a framework for time-series forecasting based on the using of ensemble technique.

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Section: Ensemble Learning Applicationsmentioning

confidence: 99%

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

Surakhi¹,

Serhan²,

Salah³

2020

Adv. sci. technol. eng. syst. j.

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show abstract

“…Hochreater and Schmidhuber proposed the LSTM network structure in 1997 [39], and it has progressed with the mushroom growth of deep-learning technology in recent years. The LSTM module is mainly composed of four parts: Input gate, forget gate, memory cell, and output gate [40]. The output of the LSTM is simultaneously affected by the hidden-layer information and the memory cell.…”

Section: Long Short-term Memory Networkmentioning

confidence: 99%

Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China

et al. 2019

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Power dispatching systems currently receive massive, complicated, and irregular monitoring alarms during their operation, which prevents the controllers from making accurate judgments on the alarm events that occur within a short period of time. In view of the current situation with the low efficiency of monitoring alarm information, this paper proposes a method based on natural language processing (NLP) and a hybrid model that combines long short-term memory (LSTM) and convolutional neural network (CNN) for the identification of grid monitoring alarm events. Firstly, the characteristics of the alarm information text were analyzed and induced and then preprocessed. Then, the monitoring alarm information was vectorized based on the Word2vec model. Finally, a monitoring alarm event identification model based on a combination of LSTM and CNN was established for the characteristics of the alarm information. The feasibility and effectiveness of the method in this paper were verified by comparison with multiple identification models.

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“…Therefore, for forecasting the data continued in the time series, the model of an RNN is trained by the continuous data to pass the features on to the next time in the timeline. For forecasting wind speed, Cheng et al present an RNN-based architecture to maintain diversities of sub-models and take advantages of learning [55]. For estimating the charging state of an Li-ion battery without using battery models, filters, and inference systems like Kalman filters, Chemali et al introduce an RNN with an LSTM model to predict the charging state, based on continuous battery life [56].…”

Section: Related Workmentioning

confidence: 99%

Occupant Comfort Management Based on Energy Optimization Using an Environment Prediction Model in Smart Homes

et al. 2019

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Occupant comfort management is an important feature of a smart home, which requires achieving a high occupant comfort level as well as minimum energy consumption. Based on a large amount of data, learning models enable us to predict factors of a mathematical model for deriving the optimal result without expensive experiments. Comfort management supports high-level comfort to the occupant in the individual indoor environment, using the optimal power consumption to run home appliances. In this paper, we propose occupant comfort management based on energy optimization, using an environment prediction model. The proposed energy optimization model provides optimal power consumption based on the proposed objective function, which requires temperature and comfort index data as the input parameters. For the input requirement, temperature prediction model and humidity prediction model are presented based on a recurrent neural network with a pre-collected dataset, including indoor and outdoor temperature and humidity sensing data. Using the predicted temperature and humidity data, the comfort index model derives the predicted mean vote value to be used in the energy optimization model with the predicted temperature data. The experimental results present an 8.43% reduction of the optimized power consumption compared to the actual power consumption using mean absolute percentage error to calculate. Moreover, the emulation of an indoor environment using optimal energy consumption presents as approximately similar to the actual data.

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Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach

Cited by 75 publications

References 58 publications

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China

Occupant Comfort Management Based on Energy Optimization Using an Environment Prediction Model in Smart Homes

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