Short-term electricity price forecasting and classification in smart grids using optimized multikernel extreme learning machine

Bisoi, Ranjeeta; Dash, P. K.; Das, Pragyan Paramita

doi:10.1007/s00521-018-3652-5

Cited by 38 publications

(17 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the last decades it has been used in variety problems like feature learning, classification, regression, and clustering [36,37,38,39,40,41,42,43]. ELM became faster and efficient for big data processing because of the improvements of the parallel computing techniques.…”

Section: Plos Onementioning

confidence: 99%

Advanced machine learning model for better prediction accuracy of soil temperature at different depths

et al. 2020

View full text Add to dashboard Cite

Soil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression trees (CART) and group method of data handling (GMDH) in estimating monthly soil temperatures at four different depths. Various combinations of climatic variables are utilized as input to the developed models. The models' outcomes are also compared with multi-linear regression based on Nash-Sutcliffe efficiency, root mean square error, and coefficient of determination statistics. ELM is found to be generally performs better than the other four alternatives in estimating soil temperatures. A decrease in performance of the models is observed by an increase in soil depth. It is found that soil temperatures at three depths (5, 10 and 50 cm) could be mapped utilizing only air temperature data as input while solar radiation and wind speed information are also required for estimating soil temperature at the depth of 100 cm.

show abstract

Section: Plos Onementioning

confidence: 99%

Advanced machine learning model for better prediction accuracy of soil temperature at different depths

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Additionally, careful considerations on EBMs pricing mechanisms are required [10]. But as electricity prices show higher volatility as compared to other commodities they are harder to predict [21]. Key challenges in using traditional forecast techniques are presented in the next section.…”

Section: Importance Of Forecasts In Electricity Balancing Marketmentioning

confidence: 99%

“…AI technologies such as state vector machines (SVM) are well equipped to perform clustering and classification tasks on high-dimensional data sets. In [21], an AI-based extreme machine learning approach was developed for presenting price thresholds to customers which gave them adequate price statuses for making informed and timely decisions. Such classifications also find applications in incorporating virtual power plants (VPPs) into EBMs.…”

Section: B Ai-based Market Price Classificationmentioning

confidence: 99%

Applications of AI-Based Forecasts in Renewable Based Electricity Balancing Markets

Hameed

Hashemi

Traholt

2021

2021 22nd IEEE International Conference on Industrial Technology (ICIT)

View full text Add to dashboard Cite

Rising environmental concerns are integrating more renewables in power systems. This increase introduces uncertainty in generation and makes it challenging to maintain a balance between demand and supply. To avoid balancing problems and consequent stability issues, better forecast models are needed as traditional techniques are not fully equipped to deal with these new challenges. Thus, artificial intelligence (AI) based forecast techniques are gaining potential recognition in the realm of electricity markets. This paper aims at investigating the state-of-art of AI applications for price forecasts in electricity balancing markets (EBMs). The focus of previous studies extended in this direction has been towards the dayahead markets, whereas studies targeting EBMs are rather scarce. This paper shows how AI-based forecasts support EBMs modeling, resulting in more secure grid integration of distributed technologies. The benefits driven from such forecasts by market participants like brokers and customers are also investigated.

show abstract

“…Atualmente é importante considerar o aumento das smart grids em nosso sistema elétrico. As smart grids são uma nova arquitetura de distribuição de energia elétrica que integra e possibilita ações a todos os usuários a ela conectados (EL-HAWARY, 2014;DI SANTO et al, 2015;BISOI et al, 2020). A partir desse novo conceito de geração e consumo de energia elétrica podem surgir distúrbios de QEE não previstos.…”

Section: Introductionunclassified

Aprendizado de Máquina Fuzzy Incremental para Classificação de Faltas em Sistemas de Potência

Santana

Leite

2020

Abakos

View full text Add to dashboard Cite

O conceito de Qualidade da Energia está relacionado a um conjunto de alterações que podem ocorrer no sistema elétrico. Tais alterações (distúrbios/faltas) podem ocorrer em várias partes do sistema de energia, sejam nas instalações elétricas dos consumidores ou no sistema supridor da concessionária, causando prejuízos financeiros a ambas as partes. Por isso, é de fundamental importância a detecção e classificação em tempo real desses distúrbios de modo automático. Para reconhecimento e classificação de padrões, são considerados modelos inteligentes evolutivos, ou seja, modelos equipados com algoritmos de aprendizado incrementais online capazes de alterar seus parâmetros e estrutura, conforme novas informações surgem em um fluxo de dados. Em particular, é considerada a modelagem evolutiva baseada em conjuntos fuzzy (FBeM). Para o pré-processamento dos dados mensurados, e extração de variáveis indicadoras da presença de distúrbios, é considerado o filtro Hodrick-Prescott, a técnica de transformada rápida de Fourier e o valor eficaz da tensão. O modelo desenvolvido neste trabalho tem alcançado um desempenho comparável àqueles de modelos estado da arte na área de qualidade de energia. Detecção e classificação de distúrbios tais como elevação de tensão, sub-harmônico, transitório oscilatório, spikes e notching, ocorrendo de forma simultânea ou não, são alcançadas com acurácia de, aproximadamente, 99%.

show abstract

Short-term electricity price forecasting and classification in smart grids using optimized multikernel extreme learning machine

Cited by 38 publications

References 40 publications

Advanced machine learning model for better prediction accuracy of soil temperature at different depths

Advanced machine learning model for better prediction accuracy of soil temperature at different depths

Applications of AI-Based Forecasts in Renewable Based Electricity Balancing Markets

Aprendizado de Máquina Fuzzy Incremental para Classificação de Faltas em Sistemas de Potência

Contact Info

Product

Resources

About