Factor decomposition and prediction of solar energy consumption in the United States

Chen, Jiandong; Yu, Jie; Song, Malin; Valdmanis, Vivian

doi:10.1016/j.jclepro.2019.06.173

Cited by 31 publications

(6 citation statements)

References 40 publications

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“…Their conclusions, namely the assessment of three schemes (no compensation for excess electricity, net metering, and net billing), which showed that all customer classes are profitable and net metering offers the most customer benefits, are useful for the national policy. Solar energy usage varies among countries (United Arab Emirates, Turkey, Spain and Germany, China, USA) owing to different environmental conditions and policy mechanisms (Chang et al 2003;Chen et al 2019;Hepbasli and Canakci 2003;Mokri et al 2013;Sanz-Casado et al 2014). The methodology, which was proposed by Chen et al (2019), was based on deep neural networks and was used for the decomposition of solar energy consumption data in the USA in 1983-2017.…”

Section: Literature Review 1a Review Of Applications Of Solar Pv Systemsmentioning

confidence: 99%

“…Solar energy usage varies among countries (United Arab Emirates, Turkey, Spain and Germany, China, USA) owing to different environmental conditions and policy mechanisms (Chang et al 2003;Chen et al 2019;Hepbasli and Canakci 2003;Mokri et al 2013;Sanz-Casado et al 2014). The methodology, which was proposed by Chen et al (2019), was based on deep neural networks and was used for the decomposition of solar energy consumption data in the USA in 1983-2017. Their findings provide insights into future demand for solar energy in the United States.…”

Section: Literature Review 1a Review Of Applications Of Solar Pv Systemsmentioning

confidence: 99%

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The economic efficiency of photovoltaic energy for energy prosumers

Olczak¹,

Matuszewska²,

Lishchenko³

et al. 2022

Polityka Energetyczna – Energy Policy Journal

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This article presents an investigation of solar power plants' economic efficiency in the case of energy prosumers. The economic effect of the development of solar energy, the environmental effect of the transition to green energy and the social effect due to lower electricity costs and investment growth from the use of photovoltaic installations (PVI) have been proven. The level of annual savings in PVI due to changes in production and own consumption of electricity are determined. Through use of factor analysis, the grouping method, the method of generalizing indicators, quantitative data collection for solar PV systems and the matrix method, the two main hypotheses were proven: (i) solar energy production should be stimulated by a sound state tariff policy; (ii) prosu-96 mers as players of the electricity market should be considered in the tariff policy. It is revealed that at current interest rates, PVI operational activity is subject to more complex factors, and the main one becomes economic, namely considering the economy of consumers, the level of taxation or grants of PVI activities, as well as productivity and the real state of technical condition of devices. The provided research develops the theoretical and empirical basis for the state policy of solar electricity usage with consideration to the peculiarities of its production and consumption. The process of production and consumption of electricity in PVI is not characterized by uniformity, which is derived from a number of factors, primarily from natural and climatic conditions. It also depends on the technical characteristics of the devices.

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Section: Literature Review 1a Review Of Applications Of Solar Pv Systemsmentioning

confidence: 99%

Section: Literature Review 1a Review Of Applications Of Solar Pv Systemsmentioning

confidence: 99%

The economic efficiency of photovoltaic energy for energy prosumers

Olczak¹,

Matuszewska²,

Lishchenko³

et al. 2022

Polityka Energetyczna – Energy Policy Journal

View full text Add to dashboard Cite

show abstract

“…Machine-learning models and deep-learning techniques, including support-vector machines [64][65][66][67][68][69][70]] and artificial neural networks (ANN) [71][72][73][74][75] have been booming data-driven prediction models. Deep learning includes CNN [76], deep neural network (DNN) [77][78][79], long short-term memory [64,[80][81][82][83][84][85], and the other hybrid models used in multistep predictions of solar energy. A method for predicting solar radiation sequences was introduced by using multiscale decomposition techniques, such as empirical mode decomposition (EMD), integrated empirical mode decomposition (EEMD), and wavelet decomposition, to investigate several clear sky index data [86], and based on linear, the method performs an autoregressive process (AR) and a nonlinear method.…”

Section: The Status Of Machine-learning Technology Used In Renewable-...mentioning

confidence: 99%

A Survey of Machine Learning Models in Renewable Energy Predictions

et al. 2020

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

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“…In other words, the energy radiated by the sun to the Earth per second is equivalent to 5 million tons of standard coal, which is more than 10,000 times the total energy consumption of the entire world (Pierce, 2016). As an In existing research, various models are used to predict energy consumption, for example, the hybrid forecasting system (Du et al, 2018), computational intelligence technology (Meenal et al, 2018), Granger causality analysis (Pinzón, 2018), NEMS model (Soroush et al, 2017), LEAP model (Dong et al, 2017), time series analysis , LSTM model (Chen et al, 2019), and grey forecasting model (Zeng et al, 2018;Wu et al, 2019;Guo et al, 2020). In all the prediction models, the grey prediction model has attracted substantial attention due to its advantages of convenient use, simple modeling process and high accuracy (Wu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Forecasting solar energy consumption using a fractional discrete grey model with time power term

Wang

2022

Clean Techn Environ Policy

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Accurate prediction of energy consumption is an important basis for policymakers to formulate and improve energy policies and measures. In this paper, a new grey prediction model FDGM(1,1, t  ) is proposed. The grey wolf optimizer (GWO) is used to optimize the fractional-order r and the time power  in the model. A numerical example and four sets of solar energy consumption data (France, South Korea, OECD, and Asia Pacific region) are used to establish the FDGM(1,1, t  ) model. Based on the idea of metabolism, the solar energy consumption of the above four economies in the next 10 years is predicted. The results show that the FDGM(1,1, t  ) model is more reliable and effective than the other seven grey models. From 2020 to 2029, the solar energy consumption in South Korea, the OECD, and the Asia Pacific region will gradually increase; the solar energy consumption in France will slowly increase in the next few years and will gradually decrease after reaching a peak in 2026. The grey prediction model FDGM(1,1, t  ) proposed in this paper has strong adaptability and can be used not only for the prediction of solar energy consumption but also for the prediction of other energy sources.

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Factor decomposition and prediction of solar energy consumption in the United States

Cited by 31 publications

References 40 publications

The economic efficiency of photovoltaic energy for energy prosumers

The economic efficiency of photovoltaic energy for energy prosumers

A Survey of Machine Learning Models in Renewable Energy Predictions

Forecasting solar energy consumption using a fractional discrete grey model with time power term

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