Forecasting of solar energy with application for a growing economy like India: Survey and implication

Mohanty, Sthitapragyan; Patra, Prashanta Kumar; Sahoo, Sudhansu S.; Mohanty, Asit

doi:10.1016/j.rser.2017.04.107

Cited by 72 publications

(32 citation statements)

References 40 publications

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“…Ardakani et al forecasted long-term electrical energy consumption [18]. Mohanty et al forecasted solar energy with application [19]. Bhattacharya et al forecasted wood energy [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

Jiang

Yang

2018

Sustainability

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Better prediction of energy demand is of vital importance for developing countries to develop effective energy strategies to improve energy security, partly because those countries' energy demands are increasing rapidly. In this work, metabolic grey model (MGM), autoregressive integrated moving average (ARIMA), MGM-ARIMA, and back propagation neural network (BP) are adopted to forecast energy demand in India, the third largest energy consumer in the world after China and the USA. The average relative errors between the actual and simulated value are 1.31% (MGM), 1.07%, 0.92% (MGM-ARIMA), and 0.39% (BP). The high prediction accuracy indicates that the prediction result is effective. The result shows that India's energy consumption will increase by 4.75% a year in the next 14 years. Compared with the 5.1% per year on average in 1995-2016, India's energy consumption will still continue its steady growth at about 5% growth from 2017 to 2030.

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“…Ardakani et al forecasted long-term electrical energy consumption [18]. Mohanty et al forecasted solar energy with application [19]. Bhattacharya et al forecasted wood energy [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

Jiang

Yang

2018

Sustainability

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“…Recent studies that try to investigate the relationships between economy and energy resources in particular and environment in general are mostly associated with specific countries, e.g., Norway, China, India, Turkey, United States, South Korea, Brazil, Germany or a specific type of renewable resource (wind, solar, tide currents) [19,[31][32][33][34][35]. Moreover, many forecasting methods are applied in current studies, ranging from grey theory prediction and time series compression to Holt's or Winter's exponential method [36].…”

Section: Modelling Of Economic Systemsmentioning

confidence: 99%

Adding Feedbacks and Non-Linearity to the Neoclassical Growth Model: A New Realm for System Dynamics Applications

Režný¹,

Bureš²

2018

Systems

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Modelling of economic systems is traditionally associated with a mathematical formalism that has its drawbacks and limitations. This study applies system dynamics as a specific modelling technique that enables us to modify and elaborate existing economic models and improve them both from a theoretical perspective and for practical applications. More specifically, the Solow-Swan growth model is enriched by feedback and non-linearity based on its extension by the energy sector. The influence and role of renewable resources are considered in this enhancement. The developed model is tested in two different scenarios and utilizes sensitivity analysis as the primary tool. Acquired outcomes offer a new perspective on the economy-energy nexus based on real data and demonstrate that system dynamics can be successfully used as a modelling tool even in the theoretical economics as a traditional discipline.

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“…Usually to predict renewable sources of energy two approaches may be used: an approach based on physical models (Badescu, 2008), using mathematical equations to describe physics and dynamics of the atmosphere that influences solar radiation, and an approach based on time series analysis by means of statistical models (Paolik (Inman et al, 2013), (Pelland, 2013), (Diagne et al, 2013), (Mohanty et al, 2017) provide good overviews on the current state of the art in solar irradiance forecasting, while (Voyant, 2017) provides a more specific review on the application of machine learning methods for solar radiation forecasting. The work proposed in (Barzin, 2016) suggests that the use of gradient boosted regression trees can be a valid solution for multi-site solar power forecasting.…”

Section: Solar Forecastmentioning

confidence: 99%

Data-Mining-based filtering to support Solar Forecasting Methodologies

Pinto

Marques

Sousa

et al. 2017

ADCAIJ

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Forecasting of solar energy with application for a growing economy like India: Survey and implication

Cited by 72 publications

References 40 publications

Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

Adding Feedbacks and Non-Linearity to the Neoclassical Growth Model: A New Realm for System Dynamics Applications

Data-Mining-based filtering to support Solar Forecasting Methodologies

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