Artificial neural network based monthly load curves forecasting

Bărbulescu, Constantin; Kilyeni, Stefan; Deacu, Antheia; Turi, Gabriel Mihai; Moga, Mirela

doi:10.1109/saci.2016.7507378

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…Figure 2 shows the typical daily load curve of a power station. The load curve can either be obtained from real-time data or predicted using forecasting algorithms such as neural networks [9]. Similarly, the yearly load curve can be obtained by using monthly load curves and is generally used to determine the annual load factor.…”

Section: Load Curvementioning

confidence: 99%

Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

et al. 2018

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This paper proposes a system model for optimal dispatch of the energy and reserve capacity considering uncertain load demand and unsteady power generation. This implicates uncertainty in managing the power demand along with the consideration of utility, user and environmental objectives. The model takes into consideration a day-ahead electricity market that involves the varying power demand bids and generates a required amount of energy in addition with reserve capacity. The lost opportunity cost is also considered and incorporated within the context of expected load not served. Then, the effects of combined and separate dispatching the energy and reserve are investigated. The nonlinear cost curves have been addressed by optimizing the objective function using robust optimization technique. Finally, various cases in accordance with underlying parameters have been considered in order to conduct and evaluate numerical results. Simulation results show the effectiveness of proposed scheduling model in terms of reduced cost and system stability.

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Section: Load Curvementioning

confidence: 99%

Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

et al. 2018

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“…The results of ARIMA model implementation for forecasting tended to be constant that was only capable to read the historical data from network activity. Barbulescu et al [10] used Artificial Neural Network (ANN) to forecast the monthly electrical load. This study provides an accuracy of 65% as the results.…”

Section: Related Studiesmentioning

confidence: 99%

“…In fuzzy logic known a condition from "0" to "1" [10]. In crisp set, the membership value on an item x in an A set is written μA [x].…”

Section: Fuzzy Logicmentioning

confidence: 99%

The Effect of External Factors on Consumption Electricity Loads Forecasting using Fuzzy Takagi-Sugeno Kang

Santika

Mahmudy

2017

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 Abstract-.This study applied Fuzzy Inference SystemSugeno to forecast electrical load by considering the external factors. To see the accuracy of forecasting using Fuzzy Inference System Sugeno, then a comparison between the forecasting results of Fuzzy Inference SystemSugenousing historical data with Fuzzy Inference SystemSugeno using external factors was done. By using external factors method, resulted the smallest RMSE of 0762 and using historical data obtained error (RMSE) of 1028. The results of the study came to the conclusion that Fuzzy Inference System Sugenomethod using external factors to forecast the consumption of electrical load givesa better result than Fuzzy Inference SystemSugeno using only historical data.Index Terms-Fuzzy Inference System Sugeno, external factors, consumption of electrical load forecasting.

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Data Decomposition Based Learning for Load Time-Series Forecasting

Bedi

Toshniwal

2020

ECML PKDD 2020 Workshops

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Artificial neural network based monthly load curves forecasting

Cited by 10 publications

References 10 publications

Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

The Effect of External Factors on Consumption Electricity Loads Forecasting using Fuzzy Takagi-Sugeno Kang

Data Decomposition Based Learning for Load Time-Series Forecasting

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