ANN based Short-Term Load Curve Forecasting

Chis, Violeta; Bărbulescu, Constantin; Kilyeni, Stefan; Dziţac, Simona

doi:10.15837/ijccc.2018.6.3385

Cited by 8 publications

(10 citation statements)

References 17 publications

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“…On the basis of the extracted weather real data sets, PV electricity is forecasted as per the actual PV electricity generated data. More accurate short-term weather information in PV electricity forecasting models could increase the accuracy of the demand response estimation [29,30]. Here, the design of BSS optimization aims to predict actual EVs load statistics into the EMS with a buffer storage system in order to maximize the utilization of PV-based electricity and minimize the fluctuation of the voltage profile on the distribution grid.…”

Section: Design and Optimization Of Proposed Emsmentioning

confidence: 99%

Intelligent energy management scheme‐based coordinated control for reducing peak load in grid‐connected photovoltaic‐powered electric vehicle charging stations

Amir

Din

Haque

et al. 2023

IET Generation Trans & Dist

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Solar‐based Distributed Generation (DG) powered Electric Vehicles (EVs) charging stations are widely adopted nowadays in the power system networks. In this process, the distribution grid faces various challenges caused by intermittent solar irradiance, peak EVs load, while controlling the state of charge (SoC) of batteries during dis(charging) phenomena. In this paper, an intelligent energy management scheme (IEMS)‐based coordinated control for photovoltaic (PV)‐based EVs charging stations is proposed. The proposed IEMS optimizes the PV generation and grid power utilization for EV charging stations (EVCS) by analysing real‐time meteorological and load demand data. The coordinated control of EMS provides power flow between PV generation, distribution grid, and EVs battery storage in a manner which results in the reduction of peak power demand by a factor of two. Further, the adaptive neuro‐based fuzzy control approach includes forecasting solar‐based electricity generation and EVs loads demand predictions to optimize IEMS according to the Indian power scenario. The proposed IEMS optimally utilizes the buffer batteries system for reducing the peak electricity demand with low system losses and reducing the impact of EVs charging load on distribution grid. The results are analysed using the digital simulation model and validated with real‐time hardware‐in‐loop experimental setup.

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Section: Design and Optimization Of Proposed Emsmentioning

confidence: 99%

Intelligent energy management scheme‐based coordinated control for reducing peak load in grid‐connected photovoltaic‐powered electric vehicle charging stations

Amir

Din

Haque

et al. 2023

IET Generation Trans & Dist

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“…There are numerous examples of using ANN for the successful modelling of the most diverse processes and activities. Activities that belong to the field of management [4] [5] or different industrial processes [6][7] can be considered. Their applications are also known in the analysis and forecasting of weather conditions and climate change [8] [9], with implications for agriculture [10].…”

Section: Simulation Of the Mechanical Properties Of The Materialmentioning

confidence: 99%

“…The results are presented in Table III. The input data were chosen so as to form certain groups:  Group 1: inputs that have the layers oriented the same (1 and 2), and the tensile forces opposedpairs of rows (1,16), (3,17), (5,18), (30,35);  Group 2: inputs that have the same values as some of the training data -(1,0), (1,45), (1,90), (2,0), (2,45), (2,90);  Group 3: input pairs that have their layers oriented the same (1 or 2), and the traction forces acting on directions symmetrical to the direction of the training forcepairs of rows (1,4), (6,9), (11,14), (20,23), (25,28), (30,33);  Group 4: the rest of the data.…”

Section: Data Used In the Validation Phase Of The Annmentioning

confidence: 99%

“…, (30,35);  Group 2: inputs that have the same values as some of the training data -(1,0), (1,45), (1,90), (2,0), (2,45), (2,90); Group 3: input pairs that have their layers oriented the same (1 or 2), and the traction forces acting on directions symmetrical to the direction of the training forcepairs of rows(1,4),(6,9),(11,14), (20,23), (25,28), (30,33);  Group 4: the rest of the data.…”

mentioning

confidence: 99%

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Determining the Mechanical Properties of a New Composite Material using Artificial Neural Networks

Ciupan¹,

Ciupan²,

Jucan³

2018

IJETT

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The paper studies the possibility of using artificial neural networks (ANN) to determine certain mechanical properties of a new composite material. This new material is obtained by a mixture of hemp and polypropylene fibres. The material was developed for the industry of upholstered furniture. Specifically, it is intended for the making of elements of the support structure of some upholstered goods (chairs, armchairs, sofa sides) with the objective of replacing wood. The paper aims to calculate the following mechanical properties: maximum tensile strength and maximum elongation.

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“…A convolutional neural network is applied in [31] for prediction. ANFIS is used for modelling complex systems in studies [32][33][34][35]. For short-term forecasting, artificial neural networks (ANN) and hybrid time-series models were employed [36].…”

Section: Introductionmentioning

confidence: 99%

Modelling and Prediction of Reactive Power at Railway Stations Using Adaptive Neuro Fuzzy Inference Systems

2022

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Electricity has become an important concern in today’s society. This is due to the fact that the electric grid now has a greater number of non-linear components. The AC-powered locomotive is one of these non-linear components. The aim of this paper was to model and predict the reactive power produced by an AC locomotive. This paper presents a study on the modelling and prediction of reactive power produced by AC-powered electric locomotives. Reactive power flow has a significant impact on network voltage levels and power efficiency. The research was conducted by using intelligent techniques—more precisely, by using the adaptive neuro fuzzy inference system (ANFIS). Several approaches to the ANFIS structure were used in the research. Of these, we mention the ANFIS-grid partition, ANFIS subtractive clustering and ANFIS fuzzy c-means (FCM) clustering. Thus; for modelling and predicting reactive power, ANFIS was trained, then tested. For the training of ANFIS, experimental data obtained from measurements performed in a train supply sub-station were used. The measurements were taken over a period of time when the locomotives were far away from the station, close to the station, and at the station, respectively. The currents and voltages from the supply substation, respectively the active, reactive, and distorted powers, were measured on the data acquisition board. With the measured data of the reactive power, the modelling with ANFIS was performed, and a prediction of the variation in the reactive power was made. The paper analysed the results of the modelling by comparing between several types of ANFIS architectures. The values of RMSE, RMS and the training time of ANFIS were compared for several structures of ANFIS.

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ANN based Short-Term Load Curve Forecasting

Cited by 8 publications

References 17 publications

Intelligent energy management scheme‐based coordinated control for reducing peak load in grid‐connected photovoltaic‐powered electric vehicle charging stations

Intelligent energy management scheme‐based coordinated control for reducing peak load in grid‐connected photovoltaic‐powered electric vehicle charging stations

Determining the Mechanical Properties of a New Composite Material using Artificial Neural Networks

Modelling and Prediction of Reactive Power at Railway Stations Using Adaptive Neuro Fuzzy Inference Systems

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