Convolution integral based multivariable grey prediction model for solar energy generation forecasting

Senapati, Rajendra Narayan; Sahoo, Niranjan; Mishra, Sukumar

doi:10.1109/pecon.2016.7951643

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…The random number seed is usually fixed to ensure the reproducibility of the results. Using suitable number for modeling with a neural network to load the data set as pandas' data frame by extracting the Numpy array from data frame, then getting the floating-point values by converting the integer values from Numpy array [36][37].…”

Section: The Data Set Descriptionmentioning

confidence: 99%

The Solar Energy Forecasting by Pearson Correlation using Deep Learning Techniques

Al-Jaafreh

Al-Odienat

2022

Earth Sciences and Human Constructions

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Solar energy is one of the most important renewable energy sources (RES) with many advantages as compared to other types of sources. Climate change is gradually becoming a global challenge for the sustainable development of humanity. There will potentially be two key features, for future electricity systems, high penetration or even dominance of renewable energy sources for clean energy e.g., onshore/offshore wind and solar PV. Solar energy forecasting is essential for the energy market. Machine learning and deep learning techniques are commonly used for providing an accurate forecasting of the energy that will be produced. The weather factors are related to each other in terms of influence, a wide range of features that are necessary to consider in the prediction process. In this paper, the effect of some atmospheric factors like Evapotranspiration and soil temperature are investigated using deep learning techniques. Higher accuracy is achieved when new features related to solar irradiation were considered in the forecasting process.

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Section: The Data Set Descriptionmentioning

confidence: 99%

The Solar Energy Forecasting by Pearson Correlation using Deep Learning Techniques

Al-Jaafreh

Al-Odienat

2022

Earth Sciences and Human Constructions

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“…The author Chow et al [18] employed 1128 high-quality data to train and test the presented model to predict the real-time energy generation [18]. The author Senapati et al [19] used a Multivariable Grey Model (GMC) for energy generation prediction using solar energy. Although much research work exists, very few approaches confronted the SPV energy generation for individual customers.…”

Section: Introductionmentioning

confidence: 99%

AI-Empowered Recommender System for Renewable Energy Harvesting in Smart Grid System

Patel¹,

Kumari

Tanwar

et al. 2022

IEEE Access

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The electric grid has already been transitioned towards a more flexible, intelligent, and interactive grid system, i.e., Smart Grid (SG) for load management, energy prediction, higher penetration of renewable energy generation, future planning, and operations. However, there is a huge gap between energy demand and supply due to the rise of different electric products and electric vehicles. Renewable Energy Harvesting (REH) plays a critical role in managing this demand response gap, where energy is generated from various renewable energy resources such as Solar PhotoVoltaic (SPV) and wind energy. Several research works exist in this regard. However, they have not yet been exploited fully. So, this paper proposed AI-RSREH approach, i.e., the AI-empowered Recommender System for REH in residential houses. The main goal of the proposed AI-RSREH approach is to predict energy generation based on SPV accurately, and this study aims to minimize the gap between the actual generation of energy and the predicted energy generation along with a recommender system for SPV installation. An exploratory residential house-wise data analytics is conducted for the demand response gap. AI-RSREH uses a stacked Long-Short Term Memory (LSTM) model to predict energy generation with a recommender system based on the energy generation prediction result. The effectiveness of the proposed approach is evaluated based on the SPV installation in residential houses and prediction accuracy compared to the existing methods.

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A hybrid prediction model based on improved multivariable grey model for long-term electricity consumption

Han

Chang

2020

Electr Eng

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Convolution integral based multivariable grey prediction model for solar energy generation forecasting

Cited by 7 publications

References 11 publications

The Solar Energy Forecasting by Pearson Correlation using Deep Learning Techniques

The Solar Energy Forecasting by Pearson Correlation using Deep Learning Techniques

AI-Empowered Recommender System for Renewable Energy Harvesting in Smart Grid System

A hybrid prediction model based on improved multivariable grey model for long-term electricity consumption

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