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Hongda Liu

Bio: Hongda Liu is an academic researcher from Harbin Engineering University. The author has contributed to research in topics: Corporate governance & Photovoltaic system. The author has an hindex of 3, co-authored 4 publications receiving 235 citations.

Papers
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Journal ArticleDOI
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.

275 citations

Journal ArticleDOI
15 Dec 2019-Energy
TL;DR: A hybrid deep learning model (LSTM-Convolutional Network) is proposed and applied to photovoltaic power prediction and shows that the hybrid prediction model has better prediction effect than the single prediction model.

217 citations

Journal ArticleDOI
TL;DR: In this paper , the authors provide an overview of the latest advances in the modification and design of UiO-66 for photocatalytic applications, including the selection of metal node, the functionalization of organic linker, defect engineering, foreign metal loading, dye sensitization, and combination with semiconductors.

81 citations

Journal ArticleDOI
TL;DR: Based on the panel data of 30 provinces and cities in China from 2008 to 2018, the authors uses the Synthetic Control Method and Differences-in-Differences method to assess the effects of carbon trading policy on achieving Carbon Neutrality.

68 citations

Journal ArticleDOI
Hongda Liu1, Qing Zhang1, Xiaoxia Qi1, Yang Han1, Fang Lu1 
TL;DR: In this article, a universal mathematical model is established for the power generation by photovoltaic (PV) modules in which both the sea conditions and the ship's integrated motion, including its basic movement along with the motion caused by rocking, are taken into account.

46 citations


Cited by
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Journal ArticleDOI
TL;DR: It is ascertained that a proposed hybrid model based on a convolution network framework can accurately predict GSR and enable energy availability to be regularly monitored over multi-step horizons when coupled with a low latency Long Short-Term Memory network.

223 citations

Journal ArticleDOI
15 Dec 2019-Energy
TL;DR: A hybrid deep learning model (LSTM-Convolutional Network) is proposed and applied to photovoltaic power prediction and shows that the hybrid prediction model has better prediction effect than the single prediction model.

217 citations

Journal ArticleDOI
TL;DR: The values of three performance evaluation indicators, MBE, MAPE, and RMSE, show that the proposed hybrid deep learning model exhibits superior performance in both forecasting accuracy and stability.

200 citations

Journal ArticleDOI
TL;DR: A comprehensive survey of the existing DL-based approaches, which are developed for power forecasting of wind turbines and solar panels as well as electric power load forecasting, and discusses the datasets used to train and test the differentDL-based prediction models, enabling future researchers to identify appropriate datasets to use in their work.
Abstract: Microgrids have recently emerged as a building block for smart grids combining distributed renewable energy sources (RESs), energy storage devices, and load management methodologies. The intermittent nature of RESs brings several challenges to the smart microgrids, such as reliability, power quality, and balance between supply and demand. Thus, forecasting power generation from RESs, such as wind turbines and solar panels, is becoming essential for the efficient and perpetual operations of the power grid and it also helps in attaining optimal utilization of RESs. Energy demand forecasting is also an integral part of smart microgrids that helps in planning the power generation and energy trading with commercial grid. Machine learning (ML) and deep learning (DL) based models are promising solutions for predicting consumers’ demands and energy generations from RESs. In this context, this manuscript provides a comprehensive survey of the existing DL-based approaches, which are developed for power forecasting of wind turbines and solar panels as well as electric power load forecasting. It also discusses the datasets used to train and test the different DL-based prediction models, enabling future researchers to identify appropriate datasets to use in their work. Even though there are a few related surveys regarding energy management in smart grid applications, they are focused on a specific production application such as either solar or wind. Moreover, none of the surveys review the forecasting schemes for production and load side simultaneously. Finally, previous surveys do not consider the datasets used for forecasting despite their significance in DL-based forecasting approaches. Hence, our survey work is intrinsically different due to its data-centered view, along with presenting DL-based applications for load and energy generation forecasting in both residential and commercial sectors. The comparison of different DL approaches discussed in this manuscript reveals that the efficiency of such forecasting methods is highly dependent on the amount of the historical data and thus a large number of data storage devices and high processing power devices are required to deal with big data. Finally, this study raises several open research problems and opportunities in the area of renewable energy forecasting for smart microgrids.

172 citations

Journal ArticleDOI
TL;DR: To improve prediction accuracy on cloudy days, the clearness-index was introduced as an input data for the LSTM model and to classify the type of weather by k-means during the data processing, where cloudy days are classified as the cloudy and the mixed(partially cloudy).
Abstract: Complicated weather conditions lead to intermittent, random and volatility in photovoltaic (PV) systems, which makes PV predictions difficult. A recurrent neural network (RNN) is considered to be an effective tool for time-series data prediction. However, when the weather changes intensely, the long-term sequence of multivariate may cause gradient vanishing (exploding) during the training of RNN, leading the prediction results to local optimum. Long short-term memory (LSTM) network is the deep structure of RNN. Due to its special hidden layer unit structure, it can preserve the trend information contained in the long-term sequence, which is allowed to solve the problems of RNN and improve performance. An LSTM-based approach is applied for short-term predictions in this study based on a timescale that encompasses global horizontal irradiance (GHI) one hour in advance and one day in advance. Inaccurate forecasts usually occur on cloudy days, and the results of ANN and SVR in the literature prove this. To improve prediction accuracy on cloudy days, the clearness-index was introduced as an input data for the LSTM model and to classify the type of weather by k-means during the data processing, where cloudy days are classified as the cloudy and the mixed(partially cloudy). NN models are established to compare the accuracy of different approaches and the cross-regional study is to prove whether the method can be generalizable. From the results of hourly forecast, the R 2 coefficient of LSTM on cloudy days and mixed days is exceeding 0.9, while the R 2 of RNN is only 0.70 and 0.79 in Atlanta and Hawaii. From the results of daily forecast, All R 2 on cloudy days is about 0.85. However, the LSTM is still very effective in improving of RNN and more accurate than other models.

160 citations