Comparative Analysis Between Particle Swarm Optimization Algorithms Applied to Price-Based Demand Response

Cavalca, Diego L.; Spavieri, Guilherme; Fernandes, Ricardo Augusto Souza

doi:10.1007/978-3-319-91253-0_31

Cited by 3 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the strong performance of this algorithm, it attracted much attention. Consequently, indepth research was performed, and several varieties of the method presented [ 17 , 18 , 19 ]. Due to its generality, this technique was applied in many other domains [ 20 , 21 , 22 ].…”

Section: Methodsmentioning

confidence: 99%

Identification of Ruminal Fermentation Curves of Some Legume Forages Using Particle Swarm Optimization

Palangi

2023

Animals

View full text Add to dashboard Cite

The modeling process has a wide range of applications in animal nutrition. The purpose of this work is to determine whether particle swarm optimization (PSO) could be used to explain the fermentation curves of some legume forages. The model suited the fermentation data with minor statistical differences (R2 > 0.98). In addition, reducing the number of iterations enhanced this method’s benefits. Only Models I and II could successfully fit the fermentability data (R2 > 0.98) in the vetch and white clover fermentation curve because the negative parameters (calculated in Models III and IV) were not biologically acceptable. Model IV could only fit the alfalfa fermentation curve, which had higher R values and demonstrated the model’s dependability. In conclusion, it is advised to use PSO to match the fermentation curves. By examining the fermentation curves of feed materials, animal nutritionists can obtain a broader view of what ruminants require in terms of nutrition.

show abstract

Section: Methodsmentioning

confidence: 99%

Identification of Ruminal Fermentation Curves of Some Legume Forages Using Particle Swarm Optimization

Palangi

2023

Animals

View full text Add to dashboard Cite

show abstract

“…Minimizing electricity cost [45], [57], [125], [148], [149], [150], [151], [152], energy consumption [57], [63], Peak to average ratio (PAR) [57], [150], and maximizing social welfare [153] and reducing environmental pollution [154] are common goals in the monitoring and control aspects of customers' loads. These goals must be achieved while taking into account the tastes of the consumers.…”

Section: ) Load Scheduling and Control At The Customer Levelmentioning

confidence: 99%

“…An intelligent algorithm, which may be either predefined [144], [145] or learnt [155], [156], can be used to reflect human preferences for the operation of household appliances. The second strategy involves putting limits on what is considered a realistic timeline [149], [150], [151]. TCLs, such as heat pumps [144], [148], [157] and water heaters [144], [157], air conditioners [144], [158], [159], battery storage systems [144], [160] and electric vehicles [144], [160], [161], [162] are common examples of appliances that are controlled in a DR setting.…”

Section: ) Load Scheduling and Control At The Customer Levelmentioning

confidence: 99%

“…TCLs, such as heat pumps [144], [148], [157] and water heaters [144], [157], air conditioners [144], [158], [159], battery storage systems [144], [160] and electric vehicles [144], [160], [161], [162] are common examples of appliances that are controlled in a DR setting. Although most papers [150], [151], [155] focus on residential buildings as consumers, there is a lot of research literature focusing on scheduling like for scheduling of different loads at small commercial buildings level is presented in [162], while charging points for smart Electric Vehicles may got overloaded at peak hours, so they need to schedule their loads and customers while considering the customer level load scheduling in order to provide a better DR service [162], and also at industrial level optimization model which consisted of multivariable price function has been used to mitigate the load problems [163].…”

Section: ) Load Scheduling and Control At The Customer Levelmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment

et al. 2023

View full text Add to dashboard Cite

Demand Response (DR) has gained popularity in recent years as a practical strategy to increase the sustainability of energy systems while reducing associated costs. Despite this, Artificial Intelligence (AI) and Machine Learning (ML), have recently developed as critical technologies for demand-side management and response due to the high complexity of tasks associated with DR, as well as huge amount of data management to take decisions very near to real time implications. Selecting the best group of users to respond, learning their attitude toward consumptions and their priorities, price optimization, monitoring and control of devices, learning to engage more and more consumers in the DR schemes, and learning how to remunerate them fairly and economically are all problems that can be tackled with the help of AI techniques. This study presents an overview of AI approaches used for DR applications. Both the Artificial Intelligence and Machine Learning algorithm(s) are employed while discussing commercial efforts (from both new and existing businesses) and large-scale innovation projects that have applied AI technologies for energy DR. Different kind of DR programs implemented in different countries are also discussed. Moreover, it also discusses the application of blockchain for DR schemes in smart grid paradigm. Discussion of the strengths and weaknesses of the evaluated AI methods for various DR tasks, as well as suggestions for further study, round out the work.INDEX TERMS Artificial intelligence, blockchain, demand response, demand side management, demand response, Internet of Things (IoT), smart grids, machine learning.

show abstract

An Explicit Investigation in Demand Side Management Based on Artificial Intelligence Techniques

Abul Kalam,

Saxena,

Zahid Hassnain

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Comparative Analysis Between Particle Swarm Optimization Algorithms Applied to Price-Based Demand Response

Cited by 3 publications

References 6 publications

Identification of Ruminal Fermentation Curves of Some Legume Forages Using Particle Swarm Optimization

Identification of Ruminal Fermentation Curves of Some Legume Forages Using Particle Swarm Optimization

Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment

An Explicit Investigation in Demand Side Management Based on Artificial Intelligence Techniques

Contact Info

Product

Resources

About