Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

Anand, Atul; Suganthi, L.

doi:10.3390/en11040728

Cited by 47 publications

(21 citation statements)

References 35 publications

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“…• The uncertain parameters (including solar irradiance, demand load, and the wholesale market prices) of the problem are predicted by ANNs [38] The examined electrical power distribution system is balanced, thus the modeling is performed only for one of the three phases.…”

Section: Tests and Resultsmentioning

confidence: 99%

A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems

et al. 2018

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Due to the recent developments in the practical implementation of remotely controlled switches (RCSs) in the smart distribution system infrastructure, distribution system operators face operational challenges in the hourly reconfigurable environment. This paper develops a stochastic Model Predictive Control (MPC) framework for operational scheduling of distribution systems with dynamic and adaptive hourly reconfiguration. The effect of coordinated integration of energy storage systems and demand response programs through hourly reconfiguration on the total costs (including cost of total loss, switching cost, cost of bilateral contract with power generation owners and responsive loads, and cost of exchanging power with the wholesale market) is investigated. A novel Switching Index (SI) based on the RCS ages and critical points in the network along with the maximum number of switching actions is introduced. Due to nonlinear nature of the problem and several existing binary variables, it is basically considered as a Mixed Integer Non-Linear Programming (MINLP) problem, which is transformed into a Mixed Integer Linear Programming (MILP) problem. The satisfactory performance of the proposed model is demonstrated through its application on a modified IEEE 33-bus distribution system.

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Section: Tests and Resultsmentioning

confidence: 99%

A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems

et al. 2018

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“…Regression methods, Kalman filter-based forecasting, artificial intelligence techniques, deep recurrent neural networks, and hybrids of these techniques have been used successfully in the forecasting of electricity demand. [5][6][7] Chen and Lee proposed an adaptive network-based fuzzy system (ANFIS) method for estimating electricity consumption in public buildings. In their study, a multiple ANFIS approach was presented to predict electricity consumption based on human activities and weather conditions.…”

Section: Related Workmentioning

confidence: 99%

Prediction of electricity energy consumption including COVID‐19 precautions using the hybrid MLR‐FFANN optimized with the stochastic fractal search with fitness distance balance algorithm

Dalcalı

Özbay

Duman

2022

Concurrency and Computation

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The increase in energy consumption is affected by the developments in technology as well as the global population growth. Increasing energy consumption makes it difficult to ensure electrical energy supply security. Meeting the energy demand can be achieved with the right planning. Proper planning is critical for both economical use of resources and low cost for the end consumer. On the other hand, erroneous estimation of demand may cause waste of resources and energy crisis. Accurate estimation is possible by accurately modeling the factors affecting electricity consumption. Apart from known factors such as seasonal conditions, days of the week and hours, modeling in extreme events such as pandemics that affect all our behaviors increases the success in modeling the future projection. This ensures that the security of electrical energy supply is carried out effectively with limited resources. For this purpose, in this study, a hybrid multiple linear regression-feedforward artificial neural network (MLR-FFANN) based algorithm model was proposed, taking into account the estimated impact of the COVID-19 pandemic on the energy consumption values of Bursa, an industrial city in Turkey. The aim of the hybrid MLR-FFANN approach was to simultaneously optimize the β polynomial for multiple linear regression and the weight and bias coefficients for the forward propagation neural network using the adaptive guided differential evolution, equilibrium optimizer, slime mold algorithm, and stochastic fractal search with fitness distance balance (SFSFDB) optimization algorithms. The success of the model whose parameters were optimized using the optimization algorithms was determined according to mean absolute error, mean absolute percentage error, and root mean square error evaluation criteria and statistical analysis of these results. According to the results of the analysis, the MLR-FFANN approach whose parameters were optimized with the SFSFDB algorithm was more successful in the training of the dataset containing the COVID-19 precautions.

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“…Positive parameter w is the inertia weight. v k i is the speed of the kth iteration of the i-th particle; x k i is the position of the i-th particle at the kth iteration; pb k i is the historical best of the k-th iteration of the i-th particle; g k b is the best of group history at the kth iteration [25]. As can be seen from the above formula, the basic PSO algorithm introduces the speed update by introducing the group's best and individual advantages.…”

Section: Improved Particle Swarm Optimization (Ipso)mentioning

confidence: 99%

Prediction and Analysis of the Relationship between Energy Mix Structure and Electric Vehicles Holdings Based on Carbon Emission Reduction Constraint: A Case in the Beijing-Tianjin-Hebei Region, China

Zhao

Fan

2019

Sustainability

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In response to air pollution problems caused by carbon emissions, electric vehicles are widely promoted in China. Since thermal power generation is the main form of power generation, the large-scale development of electric vehicles is equivalent to replacing oil with coal, which will accordingly result in carbon emissions increasing if the scale of electric vehicles exceeds a certain limit. A relationship model between regional energy mix structure and electric vehicles holdings under the constraint of carbon emission reduction is established to perform a quantitative analysis of the limitation mechanism. In order to measure the scale of the future electric vehicle market under the constraint of carbon emissions reduction, a method called Extreme Learning Machine optimized by Improved Particle Swarm Optimization (IPSO-ELM) with higher precision than Extreme Learning Machine (ELM) is proposed to predict the power structure and the trend of electric vehicle development in the Beijing-Tianjin-Hebei region from 2019–2030. The calculation results show that the maximum number of electric vehicles must not exceed 19,340,000 and 26,867,171 based on emissions reduction aims and also the predicted energy mix structure in the Beijing-Tianjin-Hebei region in 2020 and 2030. At this time, the ratio of electric vehicles to traditional car ownership is 75.6% and 78.3%. The proportion of clean energy generation should reach 0.314 and 0.323 to match a complete replacement of traditional fuel vehicles for electric vehicles. A substantial increase in clean energy generation is needed so that the large-scale promotion of electric vehicles can still achieve the goal of carbon reduction. Therefore, this article will be helpful for policy-making on electric vehicle development scale and energy mix structure in the Beijing-Tianjin-Hebei region.

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Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

Cited by 47 publications

References 35 publications

A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems

A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems

Prediction of electricity energy consumption including COVID‐19 precautions using the hybrid MLR‐FFANN optimized with the stochastic fractal search with fitness distance balance algorithm

Prediction and Analysis of the Relationship between Energy Mix Structure and Electric Vehicles Holdings Based on Carbon Emission Reduction Constraint: A Case in the Beijing-Tianjin-Hebei Region, China

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