One Day-Ahead Forecasting at Different Time Periods of Energy Production in Photovoltaic Systems Using Neural Networks

Drałus, Grzegorz; Gołębiowski, M.; Gołębiowski, L.

doi:10.1109/isem.2018.8442996

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Renewable energy sources with direct voltage are connected to the network via inverters [17,18], through which it is possible to influence losses in the network. Each inverter has a P-Q (working diagram).…”

Section: Possibilities Of Active Power Loss Reductionmentioning

confidence: 99%

Optimization of Active Power Losses in Smart Grids Using Photovoltaic Power Plants

et al. 2022

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This article addresses the reduction of power losses in smart grids. Two optimization algorithms are used in this article. The first method is the enumerative method. The second method of the optimization calculation is based on the self-organizing migrating algorithm. In the first step, the network parameters are calculated based on the input data, and then the target function is determined. In this article, the target function is used to reduce the active power losses that occur during the operation of an electric network. More specifically, we attempt to determine the reactive power with the enumerative and SOMA algorithms to reduce the value of the active power losses. This article intends to illustrate the differences between the selected optimization algorithms. As observed, the optimization algorithm determines the computation time.

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Section: Possibilities Of Active Power Loss Reductionmentioning

confidence: 99%

Optimization of Active Power Losses in Smart Grids Using Photovoltaic Power Plants

et al. 2022

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“…Microgrids offer an efficient solution for integrating renewable energy and EVs; however, the amalgamation of these elements presents substantial challenges due to their unpredictable production and consumption patterns. As a result, it is crucial to develop coordinated EV charging strategies to minimize these uncertainties and optimize the usage of renewable energy sources [1,2].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Effects of Smart Parking Infrastructure on the Electrical Power System

Medved,

Bena,

Oliinyk

et al. 2023

Energies

Self Cite

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The forthcoming surge in electric vehicle (EV) adoption demands the comprehensive advancement of associated charging infrastructure. In this study, an exploration of EV charging’s impact on the power distribution system is conducted via the simulation of a parking lot equipped with six distinct types of EVs, each showcasing unique charging curves, charging power, and battery capacities. A charging profile is synthesized and compared with laboratory-obtained data to ascertain the implications on the grid. To further understand the effects of smart parking on the power distribution system, a mathematical algorithm was created and applied to a segment of an urban electrical grid that includes 70 private residences. Basic electrical parameters were computed using the node voltage method. Four scenarios were simulated: (1) the existing distribution system, (2) the current system plus smart parking, (3) the current system plus 50% of houses equipped with 3.5 kW photovoltaic installations, and (4) the current system plus photovoltaics and smart parking. This paper examines the core distribution system parameters, namely voltage and current, across these four scenarios, and the simulation results are extensively detailed herein.

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Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAE

Yammahi¹,

Aung²

2023

Heliyon

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One Day-Ahead Forecasting at Different Time Periods of Energy Production in Photovoltaic Systems Using Neural Networks

Cited by 3 publications

References 6 publications

Optimization of Active Power Losses in Smart Grids Using Photovoltaic Power Plants

Optimization of Active Power Losses in Smart Grids Using Photovoltaic Power Plants

Assessing the Effects of Smart Parking Infrastructure on the Electrical Power System

Forecasting the concentration of NO2 using statistical and machine learning methods: A case study in the UAE

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