An innovative learning approach for solar power forecasting using genetic algorithm and artificial neural network

Pattanaik, Debasish; Madichetty, Sreedhar; Khuntia, Ganesh Prasad; Dash, Ritesh; Swain, Sarat Chandra

doi:10.1515/eng-2020-0073

Cited by 28 publications

(11 citation statements)

References 71 publications

(4 reference statements)

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“…According to their estimates, it is optimal to use the SARIMA model due to seasonal distribution to predict solar radiation (Haddad et al 2019). De Gruyter believes that it is much more convenient and accurate to predict using GA compared to the statistical method of analysis (Pattanaik et al 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting solar generation in energy systems to accelerate the implementation of sustainable economic development

Sribna¹,

Koval²,

Olczak³

et al. 2021

Polityka Energetyczna – Energy Policy Journal

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The analysis and assessment of the development of solar energy were carried out and it was noted that the production of solar electricity in the world has increased by more than 15% over the last year. In 2020 there are more than 37 countries with a total photovoltaic capacity of more than one GW, and the share of solar energy in total world electricity production was 8.15%. In the regional context, the largest production of electricity by solar energy sources is in Asia (at the expense of India and China) and North America (USA). The study assesses the main factors in the development of solar energy from the standpoint of environmental friendliness and stability of the electricity supply. The problem of the utilization of solar station equipment in the EU and the US is considered. According to the IPCC, IEA, Solar Power Europe, forecasting the development of solar energy in the world is considered. It is proved that the main factor in assessing the economic efficiency of solar energy production is a regional feature due to natural and climatic conditions (intensity of solar radiation). The use of solar generation is auxiliary for the operation of modern electrical networks as long as the efficiency of photovoltaic cells increases by at least 60-65%. Marginal costs of solar energy are minimal in those countries where active state support is provided. The competitiveness of solar energy is relatively low. However, from the standpoint of replacing energy fuel at a cost of USD 10 per 1 Gcal of solar energy saves 10-20 million tons of conventional fuel. Industrial production of solar electricity at modern solar power plants forms a price at the level of USD 250-450 for 1 MWh.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting solar generation in energy systems to accelerate the implementation of sustainable economic development

Sribna¹,

Koval²,

Olczak³

et al. 2021

Polityka Energetyczna – Energy Policy Journal

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“…[6], M5PDT, GPR [7], BPNN, BPNN-GA, ENN, etc. [8], MLP [8,10], P-PVFM [10], SVM [12], PSO-ANN [13] Yes [2,6] Yes [14] Hybrid Models ANN-PSO with K-mean clustering [16], DELM and information fusion rule combined [15], SWT and RF combined [17], ML, Image Processing, and acoustic classification-based technique [18], MLSHM and Auto-GRU [19], General ensemble model with DL technique [20], GA-ANN [21], RCC-LSTM [22] SVR [16], LSTM [22] Yes [16,20,22] Yes [17] Wind Turbine Energy Systems…”

Section: Necessity Of Dynamic/online Learningmentioning

confidence: 99%

Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction

Zamee

Won

2020

Energies

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A reasonable dataset, which is an essential factor of renewable energy forecasting model development, sometimes is not directly available. Waiting for a substantial amount of training data creates a delay for a model to participate in the electricity market. Also, inappropriate selection of dataset size may lead to inaccurate modeling. Besides, in a multivariate environment, the impact of different variables on the output is often neglected or not adequately addressed. Therefore, in this work, a novel Mode Adaptive Artificial Neural Network (MAANN) algorithm has been proposed using Spearman’s rank-order correlation, Artificial Neural Network (ANN), and population-based algorithms for the dynamic learning of renewable energy sources power generation forecasting model. The proposed algorithm has been trained and compared with three population-based algorithms: Advanced Particle Swarm Optimization (APSO), Jaya Algorithm, and Fine-Tuning Metaheuristic Algorithm (FTMA). Also, the gradient descent algorithm is considered as a base case for comparing with the population-based algorithms. The proposed algorithm has been applied in predicting the power output of a Solar Photovoltaic (PV) and Wind Turbine Energy System (WTES). Using the proposed methodology with FTMA, the error was reduced by 71.261% and 80.514% compared to the conventional fixed-sized dataset gradient descent-based training approach for Solar PV and WTES, respectively.

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“…(1) In the prediction, the developed model compares the different tools based on the performance in previous research forecasting [15,16]. ( 2) is research sets threshold values for the machine learning and time-series technique.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach on Time Series for PV-Solar Energy

Sivakumar

Neeraja²,

Rani³

et al. 2022

Advances in Materials Science and Engineering

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Solar energy is the kind of alternative energy that a photovoltaic (PV) system may generate. The PV system depends on the environmental conditions present at the moment, such as the humidity level, surface temperature of the PV system, and wind speed. These are the most important characteristics to consider when calculating the amount of electricity generated by the PV system. Therefore, the calculation of power production is not a simple task. In the meanwhile, we are working on developing the machine learning algorithm that will be used to estimate electricity production using the fundamental characteristics that are now accessible. Now, we begin the process of machine learning by developing a time series model since the essential parameters will change over time. We get preliminary information from four distinct locations around the country. We arrived at this conclusion by using the ANN and regression modeling approaches for best to create the most significant overall performance across all cities.

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An innovative learning approach for solar power forecasting using genetic algorithm and artificial neural network

Cited by 28 publications

References 71 publications

Forecasting solar generation in energy systems to accelerate the implementation of sustainable economic development

Forecasting solar generation in energy systems to accelerate the implementation of sustainable economic development

Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction

Machine Learning Approach on Time Series for PV-Solar Energy

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