Dailly Forecasting of Photovoltaic Power Using Non-Linear Auto-Regressive Exogenous Method

Louzazni, Mohamed; Mosalam, Heba

doi:10.1109/dasa51403.2020.9317071

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…In these equations,

\overline u (n) \in \mathbb{R}

and

\overline y (n) \in \mathbb{R}

define the output and input method at n as discrete timestep. Additionally,

{d_E} \ge 1

and

{d_y} \ge 1

define the memory in output and input by

\{ {d_E},{d_y}\} \in \mathbb{N}*

[30]. The main construction of NARXNN is obtainable in Figure 8, which includes a two layer feed‐forward network based on linear transfer function.…”

Section: Proposed Pv Power Forecasting Approachmentioning

confidence: 99%

“…In these equations, u(n) ∈ ℝ and y(n) ∈ ℝ define the output and input method at n as discrete timestep. Additionally, d E ≥ 1 and d y ≥ 1define the memory in output and input by {d E , d y } ∈ ℕ * [30]. The main construction of NARXNN is obtainable in Figure 8, which includes a two layer feed-forward network based on linear transfer function.…”

Section: Proposed Learnersmentioning

confidence: 99%

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Execution of synthetic Bayesian model average for solar energy forecasting

Abedinia

Bagheri

2022

IET Renewable Power Gen

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Accurate photovoltaic (PV) forecasting is quite crucial in planning and in the regular operation of power system. Stochastic habit along with the high risks in PV signal uncertainty and a probabilistic forecasting model is required to address the numerical weather prediction (NWP) underdispersion. In this study, a new synthetic prediction process based on Bayesian model averaging (BMA) and Ensemble Learning is developed. The proposed model is initiated by the improved self-organizing map (ISOM) clustering K-fold cross-validation for the training process. To provide desirable learning model for different input samples, three learners including long short-term memory (LSTM) network, general regression neural network (GRNN), and non-linear auto-regressive eXogenous NN (NARXNN) are employed. The proposed BMA approach is combined with the output of the learners to obtain accurate and desirable outcomes. Different models are precisely compared with the obtained numerical results over real-world engineering test site, that is, Arta-Solar case study. The numerical analysis and recorded results validate the performance and superiority of the proposed model.

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“…In these equations,

\overline u (n) \in \mathbb{R}

and

\overline y (n) \in \mathbb{R}

define the output and input method at n as discrete timestep. Additionally,

{d_E} \ge 1

and

{d_y} \ge 1

define the memory in output and input by

\{ {d_E},{d_y}\} \in \mathbb{N}*

[30]. The main construction of NARXNN is obtainable in Figure 8, which includes a two layer feed‐forward network based on linear transfer function.…”

Section: Proposed Pv Power Forecasting Approachmentioning

confidence: 99%

Section: Proposed Learnersmentioning

confidence: 99%

Execution of synthetic Bayesian model average for solar energy forecasting

Abedinia

Bagheri

2022

IET Renewable Power Gen

View full text Add to dashboard Cite

show abstract

“…where E D and E w are the sum of squared network errors and the sum of squared network weights, respectively. While α and β denote the regularization parameters [47], their values can be determined by:…”

Section: Bayesian Regularization Training Algorithmmentioning

confidence: 99%

Forecasting of Photovoltaic Power by Means of Non-Linear Auto-Regressive Exogenous Artificial Neural Network and Time Series Analysis

2021

Self Cite

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In this research paper, a nonlinear autoregressive with exogenous input (NARX) model of the nonlinear system based on neural network and time series analysis is proposed to deal with the one-month forecast of the produced power from photovoltaic modules (PVM). The PVM is a monocrystalline cell with a rated production of 175 watts that is placed at Heliopolis University, Bilbéis city, Egypt. The NARX model is considered powerful enough to emulate the nonlinear dynamic state-space model. It is extensively performed to resolve a variety of problems and is mainly important in complex process control. Moreover, the NARX method is selected because of its quick learning and completion times, as well as high appropriateness, and is distinguished by advantageous dynamics and interference resistance. The neural network (NN) is trained and optimized with three algorithms, the Levenberg–Marquardt Algorithm (NARX-LMA), the Bayesian Regularization Algorithm (NARX-BRA) and the Scaled Conjugate Gradient Algorithm (NARX-SCGA), to attain the best performance. The forecasted results using the NARX method based on the three algorithms are compared with experimentally measured data. The NARX-LMA, NARX-BRA and NARX-SCGA models are validated using statistical criteria. In general, weather conditions have a significant impact on the execution and quality of the results.

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Dailly Forecasting of Photovoltaic Power Using Non-Linear Auto-Regressive Exogenous Method

Cited by 2 publications

References 21 publications

Execution of synthetic Bayesian model average for solar energy forecasting

Execution of synthetic Bayesian model average for solar energy forecasting

Forecasting of Photovoltaic Power by Means of Non-Linear Auto-Regressive Exogenous Artificial Neural Network and Time Series Analysis

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