A comparative study on short-term PV power forecasting using decomposition based optimized extreme learning machine algorithm DOI Creative Commons
Manoja Kumar Behera, Niranjan Nayak

Engineering Science and Technology an International Journal, Год журнала: 2019, Номер 23(1), С. 156 - 167

Опубликована: Май 9, 2019

Solar irradiance fluctuates within a very short period of time that creates lot hindrances to estimate the injection output power into grid. During operation solar plant, short-term PV forecasting supports load dispatching, planning, and also regulatory actions. But this term is complicated problem in order solve it. This paper represents by constructing 3-stage approach which formed combining empirical mode decomposition (EMD) technique, sine cosine algorithm (SCA), extreme learning machine (ELM) technique. At initial phase proposed de-noised series obtained adopting signal filtering strategy based on EMD Next three different interval data are opted for training stage. The selected sets quarterly, half-hourly hourly observations. simulation results signify recommended technique performs an out-standing manner than conventional ones while addressing forecasting.

Язык: Английский

A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization DOI
Razin Ahmed, Victor Sreeram, Yateendra Mishra

и другие.

Renewable and Sustainable Energy Reviews, Год журнала: 2020, Номер 124, С. 109792 - 109792

Опубликована: Март 2, 2020

Язык: Английский

Процитировано

864
Forecasting: theory and practice DOI Creative Commons
Fotios Petropoulos, Daniele Apiletti,

Vassilios Assimakopoulos

и другие.

International Journal of Forecasting, Год журнала: 2022, Номер 38(3), С. 705 - 871

Опубликована: Янв. 20, 2022

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds future is both exciting challenging, with individuals organisations seeking to minimise risks maximise utilities. large number forecasting applications calls for a diverse set methods tackle real-life challenges. This article provides non-systematic review theory practice forecasting. We provide an overview wide range theoretical, state-of-the-art models, methods, principles, approaches prepare, produce, organise, evaluate forecasts. then demonstrate how such theoretical concepts are applied in variety contexts. do not claim this exhaustive list applications. However, we wish our encyclopedic presentation will offer point reference rich work undertaken over last decades, some key insights practice. Given its nature, intended mode reading non-linear. cross-references allow readers navigate through various topics. complement covered by lists free or open-source software implementations publicly-available databases.

Язык: Английский

Процитировано

571
Short-Term Photovoltaic Power Forecasting Based on Long Short Term Memory Neural Network and Attention Mechanism DOI Creative Commons
Hangxia Zhou, Yujin Zhang,

Lingfan Yang

и другие.

IEEE Access, Год журнала: 2019, Номер 7, С. 78063 - 78074

Опубликована: Янв. 1, 2019

Photovoltaic power generation forecasting is an important topic in the field of sustainable system design, energy conversion management, and smart grid construction. Difficulties arise while generated PV usually unstable due to variability solar irradiance, temperature, other meteorological factors. In this paper, a hybrid ensemble deep learning framework proposed forecast short-term photovoltaic time series manner. Two LSTM neural networks are employed working on temperature outputs forecasting, respectively. The results flattened combined with fully connected layer enhance accuracy. Moreover, we adopted attention mechanism for two adaptively focus input features that more significant forecasting. Comprehensive experiments conducted recently collected real-world datasets. Three error metrics were compare produced by model state-of-art methods, including persistent model, auto-regressive integrated moving average exogenous variable (ARIMAX), multi-layer perceptron (MLP), traditional all four seasons various horizons show effectiveness robustness method.

Язык: Английский

Процитировано

344
Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression DOI
Mahdi Sharifzadeh,

Alexandra Sikinioti-Lock,

Nilay Shah

и другие.

Renewable and Sustainable Energy Reviews, Год журнала: 2019, Номер 108, С. 513 - 538

Опубликована: Апрель 10, 2019

Язык: Английский

Процитировано

304
Taxonomy research of artificial intelligence for deterministic solar power forecasting DOI
Huaizhi Wang, Yangyang Liu, Bin Zhou

и другие.

Energy Conversion and Management, Год журнала: 2020, Номер 214, С. 112909 - 112909

Опубликована: Май 1, 2020

Язык: Английский

Процитировано

270
Advanced Methods for Photovoltaic Output Power Forecasting: A Review DOI Creative Commons
A. Mellit, Alessandro Pavan, Emanuèle Ogliari

и другие.

Applied Sciences, Год журнала: 2020, Номер 10(2), С. 487 - 487

Опубликована: Янв. 9, 2020

Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate forecasters remains challenging issue, particularly multistep-ahead prediction. Accurate PV forecasting critical in number applications, such as micro-grids (MGs), energy optimization and management, integrated smart buildings, electrical vehicle chartering. Over last decade, vast literature has been produced on this topic, investigating numerical probabilistic methods, physical models, artificial intelligence (AI) techniques. This paper aims at providing complete review recent applications AI techniques; we will focus machine learning (ML), deep (DL), hybrid these branches are becoming increasingly attractive. Special attention be paid to development application DL, well future trends topic.

Язык: Английский

Процитировано

253
Artificial Intelligence Based MPPT Techniques for Solar Power System: A review DOI Open Access
Kah Yung Yap, Charles R. Sarimuthu,

Joanne Mun-Yee Lim

и другие.

Journal of Modern Power Systems and Clean Energy, Год журнала: 2020, Номер 8(6), С. 1043 - 1059

Опубликована: Янв. 1, 2020

In the last decade, artificial intelligence (AI) techniques have been extensively used for maximum power point tracking (MPPT) in solar system. This is because conventional MPPT are incapable of global (GMPP) under partial shading condition (PSC). The output curve versus voltage a panel has only one GMPP and multiple local points (MPPs). integration AI crucial to guarantee while increasing overall efficiency performance MPPT. selection AI-based complicated each technique its own merits demerits. general, all exhibit fast convergence speed, less steady-state oscillation high efficiency, compared with techniques. However, computationally intensive costly realize. Overall, hybrid favorable terms balance between complexity, it combines advantages this paper, detailed comparison classification 6 major made based on review MATLAB/Simulink simulation results. merits, open issues technical implementations evaluated. We intend provide new insights into choice optimal

Язык: Английский

Процитировано

234
A Survey of Machine Learning Models in Renewable Energy Predictions DOI Creative Commons
Jung-Pin Lai, Yu-Ming Chang,

Chieh-Huang Chen

и другие.

Applied Sciences, Год журнала: 2020, Номер 10(17), С. 5975 - 5975

Опубликована: Авг. 28, 2020

The use of renewable energy to reduce the effects climate change and global warming has become an increasing trend. In order improve prediction ability energy, various techniques have been developed. aims this review are illustrated as follows. First, survey attempts provide a analysis machine-learning models in renewable-energy predictions. Secondly, study depicts procedures, including data pre-processing techniques, parameter selection algorithms, performance measurements, used for Thirdly, sources values mean absolute percentage error, coefficient determination were conducted. Finally, some possible potential opportunities future work provided at end survey.

Язык: Английский

Процитировано

160
Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information DOI

Zhen Hao,

Dongxiao Niu, Keke Wang

и другие.

Energy, Год журнала: 2021, Номер 231, С. 120908 - 120908

Опубликована: Май 10, 2021

Язык: Английский

Процитировано

149
A novel long term solar photovoltaic power forecasting approach using LSTM with Nadam optimizer: A case study of India DOI Creative Commons
Jatin Sharma, Sameer Soni, Priyanka Paliwal

и другие.

Energy Science & Engineering, Год журнала: 2022, Номер 10(8), С. 2909 - 2929

Опубликована: Май 11, 2022

Abstract Solar photovoltaic (PV) power is emerging as one of the most viable renewable energy sources. The recent enhancements in integration sources into grid create a dire need for reliable solar forecasting techniques. In this paper, new long‐term PV approach using long short‐term memory (LSTM) model with Nadam optimizer presented. LSTM performs better time‐series data it persists information more time steps. experimental models are realized on 250.25 kW installed capacity system located at MANIT Bhopal, Madhya Pradesh, India. proposed compared two and eight neural network different optimizers. obtained results present significant improvement accuracy 30.56% over autoregressive integrated moving average, 47.48% seasonal 1.35%, 1.43%, 3.51%, 4.88%, 11.84%, 50.69%, 58.29% RMSprop, Adam, Adamax, SGD, Adagrad, Adadelta, Ftrl optimizer, respectively. prove that methodology conclusive can be employed enhanced planning management.

Язык: Английский

Процитировано

78