Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model

Jadidi, Aydin; Menezes, Raimundo; Souza, Nilmar de; Lima, Carla Caroline Valença de

doi:10.3390/en12101891

Cited by 25 publications

(20 citation statements)

References 27 publications

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“…Fuzzy logic algorithms have been used in many studies for electric power forecasting [ 10 , 27 , 33 , 45 , 50 , 52 , 63 , 79 , 80 , 83 , 85 , 91 , 113 , 118 , 122 , 154 , 160 , 180 , 181 ] and have reached a forecasting accuracy with an average MAPE value of 4.013%.…”

Section: Classes Of Forecasting Modelsmentioning

confidence: 99%

A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

Vivas

Allende‐Cid

Salas

2020

Entropy

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Electric power forecasting plays a substantial role in the administration and balance of current power systems. For this reason, accurate predictions of service demands are needed to develop better programming for the generation and distribution of power and to reduce the risk of vulnerabilities in the integration of an electric power system. For the purposes of the current study, a systematic literature review was applied to identify the type of model that has the highest propensity to show precision in the context of electric power forecasting. The state-of-the-art model in accurate electric power forecasting was determined from the results reported in 257 accuracy tests from five geographic regions. Two classes of forecasting models were compared: classical statistical or mathematical (MSC) and machine learning (ML) models. Furthermore, the use of hybrid models that have made significant contributions to electric power forecasting is identified, and a case of study is applied to demonstrate its good performance when compared with traditional models. Among our main findings, we conclude that forecasting errors are minimized by reducing the time horizon, that ML models that consider various sources of exogenous variability tend to have better forecast accuracy, and finally, that the accuracy of the forecasting models has significantly increased over the last five years.

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Section: Classes Of Forecasting Modelsmentioning

confidence: 99%

A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

Vivas

Allende‐Cid

Salas

2020

Entropy

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“…In addition, hybridization of the optimization method and ANFIS becomes an emerging trend in current research studies to determine the most appropriate architecture and input variables for problem domains. Jadidi et al [29] integrated the non-dominated sorting genetic algorithm II (NSGA-II) and ANFIS to achieve the short-term electric power demand forecasting. Among several optimization methods, including particle swarm optimization, ant colony optimization, differential evolution, and imperialistic competitive algorithm, the combination of NSGA-II and ANFIS provided the most accurate forecasting results.…”

Section: Literature Reviewmentioning

confidence: 99%

A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry

et al. 2020

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The development of electric vehicles (EVs) has drawn considerable attention to the establishment of sustainable transport systems to enable improvements in energy optimization and air quality. EVs are now widely used by the public as one of the sustainable transportation measures. Nevertheless, battery charging for EVs create several challenges, for example, lack of charging facilities in urban areas and expensive battery maintenance. Among various components in EVs, the battery pack is one of the core consumables, which requires regular inspection and repair in terms of battery life cycle and stability. The charging efficiency is limited to the power provided by the facilities, and therefore the current business model for EVs is not sustainable. To further improve its sustainability, plug-in electric vehicle battery pack standardization (PEVBPS) is suggested to provide a uniform, standardized and mobile EV battery that is managed by centralized service providers for repair and maintenance tasks. In this paper, a fuzzy-based battery life-cycle prediction framework (FBLPF) is proposed to effectively manage the PEVBPS in the market, which integrates the multi-responses Taguchi method (MRTM) and the adaptive neuro-fuzzy inference system (ANFIS) as a whole for the decision-making process. MRTM is formulated based on selection of the most relevant and critical input variables from domain experts and professionals, while ANFIS takes part in time-series forecasting of the customized product life-cycle for demand and electricity consumption. With the aid of the FPLCPF, the revolution of the EV industry can be revolutionarily boosted towards total sustainable development, resulting in pro-active energy policies in the PEVBPS eco-system.

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“…The nondominated sorting genetic algorithm II (NSGA-II) is a fast elite multiobjective algorithm that follows the NSGA framework [40][41][42]. This method has been effectively implemented to address various water management issues.…”

Section: Model Solutionmentioning

confidence: 99%

Development of an Optimal Model for the Xiluodu-Xiangjiaba Cascade Reservoir System Considering the Downstream Environmental Flow

Dai

et al. 2020

Sustainability

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To explore the influence of the Xiluodu-Xiangjiaba cascade reservoir system on the appropriate environmental flow (AEF) of the Jinsha River, a multiobjective optimal cascade reservoir model was established with the aim of maximizing power generation while minimizing the downstream degree of AEF alteration. The AEF was determined using the range of variability approach (RVA). The optimal model was solved using an improved version of NSGA-II called INSGA2-DS. Inflows in typical normal and dry years were selected for optimization. The results show that in a normal year, power generation can be increased by 1.28% compared with that under the current regular operation conditions by prioritizing the maximization of power generation, in which case the degree of AEF alteration will increase by 13.86%. In contrast, the degree of AEF alteration will decrease by 22.53% if ecological protection is prioritized, but power generation will decrease by 0.62%. Similarly, in a dry year, power generation can be increased by 1.76% compared with that under the current regular operation conditions to maximize economic benefit, in which case, the degree of AEF alteration will increase by 4.95%. By contrast, the degree of AEF alteration can be decreased by 13.70% if the objective is AEF minimization, but power generation will decrease by 0.48%. These research results provide useful information for the formulation of ecological operation schemes involving cascade reservoirs on the Jinsha River.

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Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model

Cited by 25 publications

References 27 publications

A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry

Development of an Optimal Model for the Xiluodu-Xiangjiaba Cascade Reservoir System Considering the Downstream Environmental Flow

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