Solar photovoltaic power forecasting using optimized modified extreme learning machine technique

Behera, Manoja Kumar; Majumder, Irani; Nayak, Niranjan

doi:10.1016/j.jestch.2018.04.013

Cited by 135 publications

(87 citation statements)

References 46 publications

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“…The energy produced by the PV plant is intermittent and is highly dependent on a number of variables, such as solar irradiance, temperature and other atmospheric parameters (e.g., humidity and cloud coverage), as well as age of the equipment and operational condition [64]. According to the literature, there are numerous applications of multiple linear regression (MLR) models for energy production forecasting, such as hourly PV production estimation [65].…”

Section: Methodological Approachmentioning

confidence: 99%

Big Data for Energy Management and Energy-Efficient Buildings

Marinakis

2020

Energies

106

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European buildings are producing a massive amount of data from a wide spectrum of energy-related sources, such as smart meters’ data, sensors and other Internet of things devices, creating new research challenges. In this context, the aim of this paper is to present a high-level data-driven architecture for buildings data exchange, management and real-time processing. This multi-disciplinary big data environment enables the integration of cross-domain data, combined with emerging artificial intelligence algorithms and distributed ledgers technology. Semantically enhanced, interlinked and multilingual repositories of heterogeneous types of data are coupled with a set of visualization, querying and exploration tools, suitable application programming interfaces (APIs) for data exchange, as well as a suite of configurable and ready-to-use analytical components that implement a series of advanced machine learning and deep learning algorithms. The results from the pilot application of the proposed framework are presented and discussed. The data-driven architecture enables reliable and effective policymaking, as well as supports the creation and exploitation of innovative energy efficiency services through the utilization of a wide variety of data, for the effective operation of buildings.

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Section: Methodological Approachmentioning

confidence: 99%

Big Data for Energy Management and Energy-Efficient Buildings

Marinakis

2020

Energies

106

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“…Historical power generation data and weather type were processed, and the MSE value was decreased by at least 0.0012 [2]. Behera et al applied an accelerated particle swarm optimization-based extreme learning machine to predict photovoltaic power, and the MAPE accuracy was obtained as 1.4440% [3]. Eseye et al developed a wavelet-particle swarm optimization-support vector machine model based on SCADA data and meteorological information, This paper is organized as follows: Section 2 explains the hybrid prediction models developed for daily photovoltaic power prediction.…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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.

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“…Solar PV power predictions approaches can be globally classified into model-based and data-driven (Ernst et al, 2009;Almonacid et al, 2014;Yang et al, 2014;Antonanzas et al, 2016;Das et al, 2018;Al-Dahidi et al, 2019). Model-based approaches employ physics-based models that use representative weather variables, e.g., solar radiations, for the predictions of the solar PV power productions (Wan et al, 2015;Behera et al, 2018;Al-Dahidi et al, 2019). Despite the fact that these approaches can lead to accurate prediction results, but simplifications and assumptions in the adopted models impose uncertainty that might pose limitations on their practical implementation (Al-Dahidi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…A prediction model based on ELM technique is proposed in Behera et al (2018) combined with Incremental Conductance (IC) Maximum Power Point Tracking (MPPT) technique. Authors introduced different PSO methods to improve prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Artificial Neural Networks Learning Algorithms and Training Datasets for Solar Photovoltaic Power Production Prediction

et al. 2019

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The capability of accurately predicting the Solar Photovoltaic (PV) power productions is crucial to effectively control and manage the electrical grid. In this regard, the objective of this work is to propose an efficient Artificial Neural Network (ANN) model in which 10 different learning algorithms (i.e., different in the way in which the adjustment on the ANN internal parameters is formulated to effectively map the inputs to the outputs) and 23 different training datasets (i.e., different combinations of the real-time weather variables and the PV power production data) are investigated for accurate 1 day-ahead power production predictions with short computational time. In particular, the correlations between different combinations of the historical wind speed, ambient temperature, global solar radiation, PV power productions, and the time stamp of the year are examined for developing an efficient solar PV power production prediction model. The investigation is carried out on a 231 kW ac grid-connected solar PV system located in Jordan. An ANN that receives in input the whole historical weather variables and PV power productions, and the time stamp of the year accompanied with Levenberg-Marquardt (LM) learning algorithm is found to provide the most accurate predictions with less computational efforts. Specifically, an enhancement reaches up to 15, 1, and 5% for the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R 2) performance metrics, respectively, compared to the Persistence prediction model of literature.

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Solar photovoltaic power forecasting using optimized modified extreme learning machine technique

Cited by 135 publications

References 46 publications

Big Data for Energy Management and Energy-Efficient Buildings

Big Data for Energy Management and Energy-Efficient Buildings

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

Assessment of Artificial Neural Networks Learning Algorithms and Training Datasets for Solar Photovoltaic Power Production Prediction

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