Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting

Wen, Lulu; Zhou, Kaile; Yang, Shanlin; Lu, Xinhui

doi:10.1016/j.energy.2019.01.075

Cited by 283 publications

(131 citation statements)

References 41 publications

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“…Meanwhile, the recursive neural network (RNN) with memory function has been widely used in the prediction of time sequence and achieved good results. For examples, visual tracking [8], load dispatch of microgrid [9] and total electron content [10]. Therefore, in this paper, we developed a model based on the Elman neural network, which is a kind of RNN, to predict foF2 one hour ahead.…”

Section: Introductionmentioning

confidence: 99%

A Short-Term Forecast Model of foF2 Based on Elman Neural Network

et al. 2019

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The critical frequency foF2 of the ionosphere F2 layer is one of the most important parameters of the ionosphere. Based on the Elman neural network (ENN), this paper constructs a single station forecasting model to predict foF2 one hour ahead. In order to avoid the network falling into local minimum, the model is optimized by the improved particle swarm optimization (IPSO). The input parameters used in the model include local time, seasonal information, solar cycle information and magnetic activity information. Data of the Wuhan Station from 2008 to 2016 were used to train and test the model. The prediction results of foF2 show that the root mean square error (RMSE) of the Elman neural network model is 4.30% lower than that of the back-propagation neural network (BPNN) model. The RMSE is further reduced by 8.92% after using the IPSO to optimize the model. This indicates that the Elman neural network model optimized by the improved particle swarm optimization is superior to the BP neural network and Elman neural network in the forecast of foF2 one hour ahead at Wuhan station.

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Section: Introductionmentioning

confidence: 99%

A Short-Term Forecast Model of foF2 Based on Elman Neural Network

et al. 2019

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“…On the load side, grid connection could be considered along with a load profile. This later quantity, analogously to the environmental quantities of irradiance and temperature, has a large literature field concerning measurement and forecasting [47][48][49].…”

Section: Resultsmentioning

confidence: 99%

Computationally Efficient Modeling of DC-DC Converters for PV Applications

et al. 2020

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In this work, a computationally efficient approach for the simulation of a DC-DC converter connected to a photovoltaic device is proposed. The methodology is based on a combination of a highly efficient formulation of the one-diode model for photovoltaic (PV) devices and a state-space formulation of the converter as well as an accurate steady-state detection methodology. The approach was experimentally validated to assess its accuracy. The model is accurate both in its dynamic response (tested in full linearity and with a simulated PV device as the input) and in its steady-state response (tested with an outdoor experimental measurement setup). The model detects automatically the reaching of a steady state, thus resulting in lowered computational costs. The approach is presented as a mathematical model that can be efficiently included in a large simulation system or statistical analysis.

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“…Research in the field of thermodynamics, energy and fuels also shows that the LSTM model performs better than MLP network in forecasting aggregated power load and photovoltaic (PV) power output [49].…”

Section: Performance and Comparison Of Modelsmentioning

confidence: 99%

Groundwater Recharge Prediction Using Linear Regression, Multi-Layer Perception Network, and Deep Learning

Huang

Gao

Crosbie

et al. 2019

Water

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As the largest freshwater storage in the world, groundwater plays an important role in maintaining ecosystems and helping humans adapt to climate change. However, groundwater dynamics, such as groundwater recharge, cannot be measured directly and is influenced by spatially and temporally complex processes, models are therefore required to capture the dynamics and provide scientific advice for decision-making. This paper developed, estimated and compared the performance of linear regression, multi-layer perception (MLP) and Long Short-Term Memory (LSTM) models in predicting groundwater recharge. The experimental dataset consists of time series of annual recharge from the year 1970 to 2012, based on water table fluctuation estimates from 465 bores in the states of South Australia and Victoria, Australia. We identified the factors that influenced groundwater recharge and found that the correlation between rainfall and groundwater recharge was strongest. The linear regression model had the poorest fitting performance, with the root mean squared error (RMSE) being greater than 0.19 when various proportions of training data were considered. The MLP model outperformed the linear regression in the prediction capability, achieving RMSE = 0.11 when 80% of training data was considered. The LSTM model was found to have the best performance, whose root mean squared errors were less than 0.12 when various proportions of training data were applied. The relative importance of influential predictors was evaluated using the above three models.

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Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting

Cited by 283 publications

References 41 publications

A Short-Term Forecast Model of foF2 Based on Elman Neural Network

A Short-Term Forecast Model of foF2 Based on Elman Neural Network

Computationally Efficient Modeling of DC-DC Converters for PV Applications

Groundwater Recharge Prediction Using Linear Regression, Multi-Layer Perception Network, and Deep Learning

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