Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network

Han, Soonhung; Qiao, Yuanhua; Yan, Jie; Liu, Yongqian; Li, Li; Wang, Zheng

doi:10.1016/j.apenergy.2019.01.193

Cited by 197 publications

(81 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The block has gates that manage both the block's state and the output. It operates on an input sequence and each gate within a block uses activation units to control its triggering state, making the change of state and addition of information flowing through the block to be conditional [65]. LSTM can achieve adequate learning and memory from one layer of LSTMs.…”

Section: B Water Demand Predictionmentioning

confidence: 99%

A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

2020

View full text Add to dashboard Cite

In recent years, scarce water resources became one of the main problems that endanger human species existence and the advancement of any nation. In this research, smart water meters were implemented, distributed, and installed in a regional area in Cairo while data were collected at uniform intervals then sent to the cloud instantly. The solution paradigm uses an Internet of Things (IoT) based on micro-services and containers. The design incorporates real-time streaming and infrastructure performance optimization to store data. A second layer to analyze the acquired data was used to model water consumption using Long Short-Term Memory (LSTM). The designed LSTM is validated and tested to be utilized in the forecast of future water demand. Moreover, two alternative machine learning methods, namely Support Vector Regression and Random Forest commonly utilized in time series forecasting applications, were used for a comparative analysis of which LSTM has proven to be superior. The proper integration of the system elements is the key to the proposed system success. Based on the success of the designed system, it can be applicable on a national scale. That can enable the optimal management of consumers' demand and improve water infrastructure utilization. The proposed paradigm presents a testbed for various scenarios that can be used in water resources management.

show abstract

Section: B Water Demand Predictionmentioning

confidence: 99%

A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

2020

View full text Add to dashboard Cite

show abstract

“…The random walks of ants are affected by the traps of ant lions. Equations (16) and 17are used for modelling this assumption mathematically.…”

Section: Ant Lion Optimization Algorithmmentioning

confidence: 99%

Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information

2020

View full text Add to dashboard Cite

Solar energy is a safe, clean, environmentally-friendly and renewable energy source without any carbon emissions to the atmosphere. Therefore, there are many studies in the field of solar energy in order to obtain the maximum solar radiation during the day time, to estimate the amount of solar energy to be produced, and to increase the efficiency of solar energy systems. In this study, it was aimed to predict the daily photovoltaic power production using air temperature, relative humidity, total horizontal solar radiation and diffuse horizontal solar radiation parameters as multi-tupled inputs. For this purpose, grey wolf, ant lion and whale optimization algorithms were integrated to the multilayer perceptron. In addition, the effects of sigmoid, sinus and hyperbolic tangent activation functions on the prediction performance were analyzed in detail. As a result of overall accuracy indictors achieved, the grey wolf optimization algorithm-based multilayer perceptron model was found to be more successful and competitive for the daily photovoltaic power prediction. Furthermore, many meaningful patterns were revealed about the constructed models, input tuples and activation functions.

show abstract

“…Accurate long-term generation of wind power prediction is of great significance for improving power grid planning, optimizing power dispatching, management development and enhancement of power consumption. High-precision wind power generation prediction is also a key factor as well as an effective way to realize power mutual assistance and power generation complementary dispatching in the field of renewable energy [4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model

Liu

Zhao

et al. 2020

Energies

View full text Add to dashboard Cite

Wind power generation is one of the renewable energy generation methods which maintains good momentum of development at present. However, its extremely intense intermittences and uncertainties bring great challenges to wind power integration and the stable operation of wind power grids. To achieve accurate prediction of wind power generation in China, a hybrid prediction model based on the combination of Wavelet Decomposition (WD) and Long Short-Term Memory neural network (LSTM) is constructed. Firstly, the nonstationary time series is decomposed into multidimensional components by WD, which can effectively reduce the volatility of the original time series and make them more stable and predictable. Then, the components of the original time series after WD are used as input variables of LSTM to predict the national wind power generation. Forty points were used, 80% as training samples and 20% as testing samples. The experimental results show that the MAPE of WD-LSTM is 5.831, performing better than other models in predicting wind power generation in China. In addition, the WD-LSTM model was used to predict the wind power generation in China under different development trends in the next two years.

show abstract

Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network

Cited by 197 publications

References 18 publications

A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information

A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model

Contact Info

Product

Resources

About