A comparative Study of Forecasting the Electrical Demand in Basra city using Box-Jenkins and Modern Intelligent Techniques

Al-zahra, Khadeega Abd; Moosa, Khulood; Jasim, Basil H.

doi:10.33762/eeej.2015.102733

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…(2015) studied the monthly electrical consumption in Basra (January 2005 to December 2011) by Auto-Regressive Integrated Moving Average (ARIMA) model and two layered feed forward artificial neural network (ANN) with 12 neurons in the hidden layer, and finally an adaptive neuro-fuzzy inference system (ANFIS) model [29]. They managed the mean absolute errors (MAE) of 0,31604 (Box-Jenkins ARIMA), 0,301 (ANN), and 0,2491 (ANFIS) [29]. Arfoa (2015) studied the maximum load (2005 to 2013) (MW) and forecasted peak load demand (2014 to 2023) in Ma'an, Karak and Aqaba in Jordan by least squares method [27].…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

An Experimental Fuzzy Inference System for Global Grid Electricity Peak Power Load Forecasting Third Core Module of First Console on G2P3S

Saraçoğlu¹

2017

Journal of Energy Systems

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Our World gives several symptoms of climate change. Devastating draughts increase (negative for World (-)), global mean temperature increase (-), lightning strikes increase (-), sea ice cover melt (-), tree mortality increase (-), and forest degradation increase (-) have been observed for decades. They are all negative measures for continuity of life. Diversity of species has been decreasing, so that life on Earth is dying. Only responsible specie for this situation is humankind. This study presents a small footstep to prevent this situation. Modeling of a 100% renewable power grid on World (Global Grid) is eminent. Annual peak power load (Gigawatt: GW, Kilowatt: kW) (peak demand or load) forecasting in power demand side is crucial for global grid modeling. This study presents an experimental fuzzy inference system for the third core module (100 years' power demand forecasting) of the first console (long term prediction) of Global Grid Peak Power Prediction System (G 2 P 3 S). The inputs (world population, global annual temperature anomalies °C) and the output (annual peak power load demand of Global Grid in GW) are modeled with seven triangular fuzzy input membership functions and seven constant output membership functions. The constant Sugeno-Type fuzzy inference system is used in the current experimental model. The maximum absolute percentage error (MAP) is calculated as 45%, and the mean absolute percentage error (MAPE) is found as 39% in this experimental study. The MAP and MAPE of the first core module model (Type 1) were 0,46 and 0,36. The MAP and MAPE of the second core module model (Interval Type 2) were 0,46 and 0,36. As a result, this study is a good start for the third core module of the first console on Global Grid Peak Power Prediction System research, development, demonstration, & deployment (RD 3 ) project. This experimental study also warns humankind in this subject. Hopefully, the most polluting societies on our World such as China,

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Section: Literature Review and Backgroundmentioning

confidence: 99%

An Experimental Fuzzy Inference System for Global Grid Electricity Peak Power Load Forecasting Third Core Module of First Console on G2P3S

Saraçoğlu¹

2017

Journal of Energy Systems

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“…In [1], the author proposed an autoregressive moving average model with an exogenous weather variable (ARMAX) to forecast short-term electrical loads (next 24 hours) using cargo results show that the mean absolute percentage error (MAPE) ranges from 3.01 to 4.54%. The comparative study of electricity demand forecast of Basra, Iraq using Box-Jenkins method and artificial neural network (ANN) was reported [2]. The obtained results have shown that using ANN improves the accuracy of the forecast.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Electricity Load Forecasting Based on Temporal Fusion Transformer Model

et al. 2022

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Electricity load forecasting plays an important role in the operation of power systems. Inaccurate forecast would reduce the safety of power supply and affect the economic and social activities as well as national defense and security. In addition, the forecast results also support decision-making on electricity generation and market transactions. Traditional methods such as AR, ARIMA, SARIMA have been widely used to forecast short term electricity load. Recently, load forecasting based on artificial and deep neural networks have shown significant accuracy improvement over traditional statistical models. In this research, a novel recurrent neural network named temporal fusion transformer (TFT) is used to forecast short-term electricity load of Hanoi city. The TFT is a newly developed model and it combines the advantages of several other RNN models such as LSTM and the self-attention mechanism. In addition to historical load data, we use temperature and humidity features, and time features such as calendar month, lunar month, days of the week, hours of the day and holidays. The forecast results of TFT are compared with traditional statistical models as well as well-known RNN models. The compared results show that the proposed method is better than other methods in both MAE and MAPE criteria.INDEX TERMS power systems, load forecasting, artificial intelligence, recurrent neural network, temporal fusion transformer.

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“…The growth of the human population and the development and application of technology has resulted in a rapid increase in electrical energy consumption. As a result, predicting electrical energy consumption is required when making electrical energy management decisions [1]. Predicting electricity consumption is critical for developing-country governments especially to improve energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Model for Predicting Electrical Energy Consumption Using ARIMA Method

Fathin¹,

Widhiyasana²,

Syakrani³

2021

Advances in Engineering Research

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The growth of the human population and technology has led to a rapid increase in electrical energy consumption. Excess electrical energy would be detrimental to the provider, whereas providing less would be detrimental to the consumers. One method for reducing these losses is to forecast the amount of electrical energy that must be available to meet demand. Prediction results can help with three types of decisions, depending on the prediction period: operational decisions (short-term), tactical decisions (medium-term), and strategic decisions (long-term). Short-term forecasts are less relevant given the urgency of the situation. This study aims to help electricity providers to make decisions by making medium and long-term predictions using the Auto-Regressive Integrated Moving Average (ARIMA) method. In the best order determination experiment, ARIMA (8,2,0) was found to be the best model with the smallest error. ARIMA (8,2,0) has an average percentage error of 5.3 percent based on the overall prediction results. There is no linearity between accuracy and prediction period in the prediction period experiment. According to the experimental results, the highest accuracy is obtained in the medium term (monthly) with a value of RMSE 753,983.98. As a result, based on the time period, ARIMA is the best for tactical decisions (medium-term) regarding electrical energy consumption.

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A comparative Study of Forecasting the Electrical Demand in Basra city using Box-Jenkins and Modern Intelligent Techniques

Cited by 6 publications

References 4 publications

An Experimental Fuzzy Inference System for Global Grid Electricity Peak Power Load Forecasting Third Core Module of First Console on G2P3S

An Experimental Fuzzy Inference System for Global Grid Electricity Peak Power Load Forecasting Third Core Module of First Console on G2P3S

Short-Term Electricity Load Forecasting Based on Temporal Fusion Transformer Model

Model for Predicting Electrical Energy Consumption Using ARIMA Method

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