Long-Term Load Forecasting of Southern Governorates of Jordan Distribution Electric System

Arfoa, Aouda

doi:10.4236/epe.2015.75023

Cited by 10 publications

(13 citation statements)

References 4 publications

(4 reference statements)

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“…The research has only discussed and investigated aggregated demand in Jordan at high voltage [23] or national level [24][25][26][27], and to the best of the author's knowledge there are no studies discussing low voltage or household demands. In Jordan, the peak demand at high voltage level shows significant seasonal variations a with two-peak pattern, where the peak demand mainly occurs during the hot summer and cold winter days due to the increase in use of air conditioning and electrical heaters [23].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In Jordan, the peak demand at high voltage level shows significant seasonal variations a with two-peak pattern, where the peak demand mainly occurs during the hot summer and cold winter days due to the increase in use of air conditioning and electrical heaters [23]. In [25][26][27], yearly forecast models for Jordan's national demand are presented using, for example, Least Squares Method [25], ANN [26] and ARX [27]. However, these studies did not estimate the hourly demand, PV output or LV demand and did not investigate relationships between demand and the different exogenous variables or calendar terms based on the nature of Jordan.…”

Section: Literature Reviewmentioning

confidence: 99%

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Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

et al. 2021

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More and more households are using renewable energy sources, and this will continue as the world moves towards a clean energy future and new patterns in demands for electricity. This creates significant novel challenges for Distribution Network Operators (DNOs) such as volatile net demand behavior and predicting Low Voltage (LV) demand. There is a lack of understanding of modern LV networks’ demand and renewable energy sources behavior. This article starts with an investigation into the unique characteristics of householder demand behavior in Jordan, connected to Photovoltaics (PV) systems. Previous studies have focused mostly on forecasting LV level demand without considering renewable energy sources, disaggregation demand and the weather conditions at the LV level. In this study, we provide detailed LV demand analysis and a variety of forecasting methods in terms of a probabilistic, new optimization learning algorithm called the Golden Ratio Optimization Method (GROM) for an Artificial Neural Network (ANN) model for rolling and point forecasting. Short-term forecasting models have been designed and developed to generate future scenarios for different disaggregation demand levels from households, small cities, net demands and PV system output. The results show that the volatile behavior of LV networks connected to the PV system creates substantial forecasting challenges. The mean absolute percentage error (MAPE) for the ANN-GROM model improved by 41.2% for household demand forecast compared to the traditional ANN model.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

et al. 2021

View full text Add to dashboard Cite

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“…In this study, a least square method is used for forecasting mean values of the load currents considering the two double-circuit OHTLs, 400 and 132 kV. This method is recommended in the literature in different researches such as [27,28]. Generally, the long-term mean load demand is essential for the system planning as well as for the economic and secure operation of the system.…”

Section: Forecasting Methods Of Induced Ac Voltagesmentioning

confidence: 99%

Location estimation of coating defects and mitigation of induced AC voltages along buried gas pipeline

Kamar¹,

Abd‐Elhady

Sabiha

et al. 2018

IET Science, Measurement & Technology

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“…On the customer side, "flexible/dispatchable loads" can adjust their own electric energy usage on the basis of a real-time set point. With the growing share of these intermittent energy sources and flexible/dispatchable loads, it becomes much more difficult to maintain the power balance [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Fault Detection and Analysis for Large Photovoltaic Systems Using Drones and Machine Vision

et al. 2018

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One of the most important sources of clean energy in the future is expected to be solar energy which is considered a real time source. Research efforts to optimize solar energy utilization are mainly concentrated on the components of solar energy systems. Photovoltaic (PV) modules are considered the main components of solar energy systems and PVs’ operations typically occur without any supervisory mechanisms, which means many external and/or internal obstacles can occur and hinder a system’s efficiency. To avoid these problems, the paper presents a system to address and detect the faults in a PV system by providing a safer and more time efficient inspection system in real time. In this paper, we proposing a real time inspection and fault detection system for PV modules. The system has two cameras, a thermal and a Charge-Coupled Device CCD. They are mounted on a drone and they used to capture the scene of the PV modules simultaneously while the drone is flying over the solar garden. A mobile PV system has been constructed primarily to validate our real time proposed system and for the proposed method in the Digital Image and Signal Processing Laboratory (DISPLAY) at Western Michigan University (WMU). Defects have been detected accurately in the PV modules using the proposed real time system. As a result, the proposed drone mounted system is capable of analyzing thermal and CCD videos in order to detect different faults in PV systems, and give location information in terms of panel location by longitude and latitude.

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Long-Term Load Forecasting of Southern Governorates of Jordan Distribution Electric System

Cited by 10 publications

References 4 publications

Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

Location estimation of coating defects and mitigation of induced AC voltages along buried gas pipeline

Unsupervised Fault Detection and Analysis for Large Photovoltaic Systems Using Drones and Machine Vision

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