Journal ArticleDOI
Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network
TLDR
In this article, a hybrid method for deterministic PV power forecasting based on wavelet transform (WT) and deep convolutional neural network (DCNN) is firstly proposed in order to reduce the negative impacts of PV energy on electric power and energy systems.About:
This article is published in Energy Conversion and Management.The article was published on 2017-12-01. It has received 253 citations till now. The article focuses on the topics: Probabilistic forecasting & Probabilistic logic.read more
Citations
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Journal ArticleDOI
Solar photovoltaic generation forecasting methods: A review
TL;DR: In this article, an extensive review on recent advancements in the field of solar photovoltaic power forecasting is presented, which aims to analyze and compare various methods of solar PV power forecasting in terms of characteristics and performance.
Journal ArticleDOI
A review of deep learning for renewable energy forecasting
TL;DR: A comprehensive and extensive review of renewable energy forecasting methods based on deep learning to explore its effectiveness, efficiency and application potential and the current research activities, challenges, and potential future research directions are explored.
Journal ArticleDOI
A review and evaluation of the state-of-the-art in PV solar power forecasting:Techniques and optimization
TL;DR: In this paper, the authors reviewed and evaluated contemporary forecasting techniques for photovoltaics into power grids, and concluded that ensembles of artificial neural networks are best for forecasting short-term PV power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty.
Journal ArticleDOI
State of the Art of Machine Learning Models in Energy Systems, a Systematic Review
Amir Mosavi,Amir Mosavi,Amir Mosavi,Mohsen Salimi,Sina Ardabili,Timon Rabczuk,Shahaboddin Shamshirband,Annamária R. Várkonyi-Kóczy +7 more
TL;DR: There is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models.
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A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network
Kejun Wang,Xiaoxia Qi,Hongda Liu +2 more
TL;DR: The results showed that when the input sequence is increased, the accuracy of the model is improved, and the prediction effect of the hybrid model is the best, followed by that of convolutional neural network.
References
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Journal ArticleDOI
Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN
Heng Shi,Minghao Xu,Ran Li +2 more
TL;DR: A novel pooling-based deep recurrent neural network is proposed in this paper which batches a group of customers’ load profiles into a pool of inputs and could address the over-fitting issue by increasing data diversity and volume.
Journal ArticleDOI
Deep learning based ensemble approach for probabilistic wind power forecasting
TL;DR: The proposed ensemble approach has been extensively assessed using real wind farm data from China, and the results demonstrate that the uncertainties in wind power data can be better learned using the proposed approach and that a competitive performance is obtained.
Journal ArticleDOI
A short-term building cooling load prediction method using deep learning algorithms
Cheng Fan,Fu Xiao,Yang Zhao +2 more
TL;DR: Wang et al. as mentioned in this paper investigated the potential of one of the most promising techniques in advanced data analytics, i.e., deep learning, in predicting 24-h ahead building cooling load profiles.
Journal ArticleDOI
A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output
TL;DR: A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output is presented and achieves better prediction accuracy than the simple SVR and traditional ANN methods.
Journal ArticleDOI
Deep belief network based deterministic and probabilistic wind speed forecasting approach
TL;DR: The comparative results demonstrate that the high-level nonlinear and non-stationary feature in the wind speed series can be learned better, and competitive performance can be obtained, and it is convinced that the proposed method has a high potential for practical applications in electric power and energy systems.