Short Term Forecasting of Global Solar Irradiance by k-Nearest Neighbor Multilayer Backpropagation Learning Neural Network Algorithm

Chen, Chao Rong

doi:10.1145/3121360.3121362

Cited by 8 publications

(7 citation statements)

References 25 publications

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“…Methodology Employed Metric Results (MSE) [22] DRNN-LSTM 0.0754 [27] CNN-LSTM 0.4188 [31] Deep LSTM 0.6031 [35] DWT-CNN-LSTM 0.6210 [36] LSTM 0.7624 [33] LSTM RNN 0.086 [50] GRU 0.9835…”

Section: Referencementioning

confidence: 99%

“…An ensemble model was developed to combine the wavelet strategy with a recurrent predictor model. The wavelet technique was employed to split the input data into various intrinsic components, and the GRU was employed over each component to perform prediction [50]. The K-nearest neighbors (KNN) algorithm was adopted to pre-process the input data, and then the forecasting was performed by BPNN.…”

Section: Introductionmentioning

confidence: 99%

“…The performance metric results of the proposed models. Comparative Analysis with Existing Works of LiteratureThe performance of the proposed multi scale solar irradiance forecasting is compared with the existing works of literature such as ESO[34], PSO-SVR[47], and GRU[50]. For hourly time scale forecasting, the performance of the proposed hybrid CSO-GWO LSTM model has reported 3.99% improved MSE, 5.05% of MAPE and 6.31% of better DA value than the ESO neural network strategy.…”

mentioning

confidence: 96%

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Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting

Jayalakshmi

Shankar²,

Subramaniam

et al. 2021

Energies

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Solar irradiance forecasting is an inevitable and most significant process in grid-connected photovoltaic systems. Solar power is highly non-linear, and thus to manage the grid operation efficiently, with irradiance forecasting for various timescales, such as an hour ahead, a day ahead, and a week ahead, strategies are developed and analysed in this article. However, the single time scale model can perform better for that specific time scale but cannot be employed for other time scale forecasting. Moreover, the data consideration for single time scale forecasting is limited. In this work, a multi-time scale model for solar irradiance forecasting is proposed based on the multi-task learning algorithm. An effective resource sharing scheme between each task is presented. The proposed multi-task learning algorithm is implemented with a long short-term memory (LSTM) neural network model and the performance is investigated for various time scale forecasting. The hyperparameter estimation of the proposed LSTM model is made by a hybrid chicken swarm optimizer based on combining the best features of both the chicken swarm optimization algorithm (CSO) and grey wolf optimization (GWO) algorithm. The proposed model is validated, comparing existing methodologies for single timescale forecasting, and the proposed strategy demonstrated highly consistent performance for all time scale forecasting with improved metric results.

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“…Methodology Employed Metric Results (MSE) [22] DRNN-LSTM 0.0754 [27] CNN-LSTM 0.4188 [31] Deep LSTM 0.6031 [35] DWT-CNN-LSTM 0.6210 [36] LSTM 0.7624 [33] LSTM RNN 0.086 [50] GRU 0.9835…”

Section: Referencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 96%

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Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting

Jayalakshmi

Shankar²,

Subramaniam

et al. 2021

Energies

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“…Kartini and Chen 2017 [24] presented a combinational solar irradiance forecasting model based on a multilayer backpropagation neural network and K-nearest neighbor algorithm: remark: lacking in comparative analysis. Madhiarasan and Deepa 2017 [25] carried out wind speed and solar irradiance forecasting using echo state network with GSANPSO (gravitational search algorithm new particle swarm optimization) based on optimized parameters and weights.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting

Madhiarasan

Louzazni

Roy

2021

International Journal of Photoenergy

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To forecast solar irradiance with higher accuracy and generalization capability is challenging in the photovoltaic (PV) energy system. Meteorological parameters are highly influential in solar irradiance, leading to intermittent and randomicity. Forecasting using a single neural network model does not have sufficient generalization ability to achieve the optimal forecasting of solar irradiance. This paper proposes a novel cooperative multi-input multilayer perceptron neural network (CMMLPNN) to mitigate the issues related to generalization and meteorological effects. Authors develop a proposed forecasting neural network model based on the amalgamation of two inputs, three inputs, four inputs, five inputs, and six inputs associated multilayer perceptron neural network. In the proposed forecasting model (CMMLPNN), the authors overcome the variance based on the meteorological parameters. The amalgamation of five multi-input multilayer perceptron neural networks leads to better generalization ability. Some individual multilayer perceptron neural network-based forecasting models outperform in some situations, but cannot assure generalization ability and suffer from the meteorological weather condition. The proposed CMMLPNN (cooperative multi-input multilayer perceptron neural network) achieves better forecasting accuracy with the generalization ability. Therefore, the proposed forecasting model is superior to other neural network-based forecasting models and existing models.

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“…Some methods were used artificial intelligent techniques such as artificial neural network as in [16][17][18][19][20]. Other methods used data mining techniques like support vector machine [21][22][23][24], K-nearest neighbor as in [25][26][27]. In addition, there are some optimization techniques such as genetic algorithm and particle swarm in were used to predict and improve the solar system depending on environmental factors such as the temperature, wind, and cloud [28][29][30][31].…”

Section: Fig 3 Pv and Wind Energy Project Distributionmentioning

confidence: 99%

An Efficient Intelligent Power Detection Method for Photovoltaic System

Mansour

Abdallah

Obeidat

2020

International Journal of Circuits, Systems and Signal Processing

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Jordan has experienced a significant increase in both peak load and annual electricity demand within the last decade due to the growth of the economy and population. Photovoltaic (PV) system is one of the most popular renewable energy source in Jordan. PV system is highly nonlinear with unpredictable behavior since it is always subject to many external factors such as severe weather conditions, irradiance level, sheds, temperature, etc. This makes it difficult to maintain maximum power production around its operation ranges. In this paper, an intelligent technique is used to predict and identify the working ability of the PV system under different weather factors in Tafila Technical University (TTU) in Jordan. It helps in optimizing power productions for different operation points. The PV system in Tafila with size 1 MWp PV generated 5.4 GWh since 2017. It saves about € 1.5 million in three years. A real power data from the PV system and a weather data from world weather online site of TTU location are used in this study. Decision tree technique is employed to identify the relation between the output power and weather factors. The results show that the system accuracy is 82.01% during the training phase and 93.425 % on the validation set.

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Short Term Forecasting of Global Solar Irradiance by k-Nearest Neighbor Multilayer Backpropagation Learning Neural Network Algorithm

Cited by 8 publications

References 25 publications

Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting

Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting

Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting

An Efficient Intelligent Power Detection Method for Photovoltaic System

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