Peak load evaluation based on the accumulated annual energy

Tawalbeh, Nabeel; El-Khazali, Reyad

doi:10.1109/melcon.2012.6196492

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…The authors (Tawalbeh & El-Khazali, 2012), in their study, presented a non-linear method to estimate the consumed energy in distribution feeders. The proposed procedure uses the statistical solution algorithm to analyse the active energy monthly consumption, which enables the estimation of energy consumption during any period of the year.…”

Section: Review Of Electric Power Load Forecastingmentioning

confidence: 99%

GCC electrical long-term peak load forecasting modeling using ANFIS and MLR methods

Al-Hamad¹,

Qamber

2019

Arab Journal of Basic and Applied Sciences

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It is important to develop a suitable model to calculate electricity demand forecasting requested by decision makers. The present study deals with the electrical long-term peak load demand forecasting using a developed Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) methods. The MLR model is formulated as a function of population and Gross Domestic Product (GDP) for the Gulf Cooperation Council (GCC) region. The Neuro-Fuzzy is thereafter trained using previous sets of data. This training gives a future annual electricity load prediction. The results obtained from the developed models have an acceptable level of mean errors. In general, the GCC region has high-energy consumption influenced by a number of factors, such as population and GDP. The annual variation of both population and GDP growth scenarios is based on development in the country. The obtained results will encourage the GCC through the energy field development and setting the future plans for it. The novelty of the present study is to avoid an increase in generation capacity in mid-term and long-term plans, which will help the GCC countries to avoid load shedding and meet the energy demands in different sectors. The developed models will help the economic development of the GCC countries. It also helps with finding the optimum time for electrical energy trading. The obtained results for the GCC illustrate the average percentage error calculated which was found to be close to 2% and 0.53% in multilinear regression and Neuro-Fuzzy, respectively. These results reduce capital investment, limiting the equipment installed and the expected load needed for better load distribution in the region. In conclusion, the Neuro-Fuzzy is the most accurate technique compared with MLR to estimate future electricity demand and, at the same time, it can be used in power system planning and development.

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Section: Review Of Electric Power Load Forecastingmentioning

confidence: 99%

GCC electrical long-term peak load forecasting modeling using ANFIS and MLR methods

Al-Hamad¹,

Qamber

2019

Arab Journal of Basic and Applied Sciences

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“…Tawalbeh et al [5] in their paper presented a nonlinear approach to estimate the consumed energy in distribution feeders. The proposed method uses the statistical solution algorithm to analyze the active energy monthly consumption, which enables one to estimate the energy consumption during any period of the year.…”

Section: Literature Reviewmentioning

confidence: 99%

Peak Load Modeling for Kingdom of Bahrain

Qamber

2012

JSEA

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Deriving some models to estimate the electrical demand for future for the Kingdom of Bahrain is carried out in the present study. The ambient temperature is taken into the account as well as the time factor (Year). The model was developed in away describing the electric power demand during a summer period. The estimated values of the maximum electrical load is obtained and evaluated on actual peak load data of the Kingdom of Bahrain

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“…The models derived in away describing the electric power demand during summer period. Tawalbeh, et al [4] in their study, presented a nonlinear method to estimate the consumed energy in distribution feeders. The proposed procedure uses the statistical solution algorithm to analyze the active energy monthly consumption, which enables one to estimate the energy consumption during any period of the year.…”

mentioning

confidence: 99%

Electric Energy Management Modeling for Kingdom of Bahrain

Qamber¹,

Al-Hamad²,

Karim³

2015

JEPE

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Abstract:In the deregulated economy, the maximum load forecasting is important for the electric industry. Many applications are included such as the energy generation and purchasing. The aim of the present study is to find the most suitable models for the peak load of the Kingdom of Bahrain. Many mathematical methods have been developing for maximum load forecasting. In the present paper, the modeling of the maximum load, population and GDP (gross domestic product) versus years obtained. The curve fitting technique used to find that models, where Graph 4.4.2 as a tool used to find the models. As well, Neuro-Fuzzy used to find the three models. Therefore, three techniques are used. These three are exponential, linear modeling and Neuro-Fuzzy. It is found that, the Neuro-Fuzzy is the most suitable and realistic one. Then, the linear modeling is the next suitable one.

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Peak load evaluation based on the accumulated annual energy

Cited by 3 publications

References 8 publications

GCC electrical long-term peak load forecasting modeling using ANFIS and MLR methods

GCC electrical long-term peak load forecasting modeling using ANFIS and MLR methods

Peak Load Modeling for Kingdom of Bahrain

Electric Energy Management Modeling for Kingdom of Bahrain

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