Short-Term Load Forecasting: The Similar Shape Functional Time-Series Predictor

Paparoditis, Efstathios; Sapatinas, Theofanis

doi:10.1109/tpwrs.2013.2272326

Cited by 82 publications

(39 citation statements)

References 25 publications

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“…Previsão por Modelos em Paralelo: Consiste em modelar perfil por meio de conjuntos de modelos similares em paralelo (um modelo para cada hora do dia). Uma desvantagem desta abordagem é a de que, por considerar cada hora em separado, os modelos não exploram suficientemente a autocorrelação entre as cargas em diferentes horas do perfil (Paparoditis e Sapatinas, 2012). Previsão por modelo único (saída multivariada): Consiste em usar um modelo único com 24 saí-das, cada uma responsável pela previsão do perfil diário e as entradas do modelo em geral incluem todas as cargas do perfil do dia antes da previsão.…”

Section: Previsão De Energia Elétricaunclassified

Uso Do Gráfico De Controle Para Minimizar Erros De Previsão Em Séries De Energia Elétrica

Christo¹,

Ferreira²

2013

Engevista

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Resumo: Em 2001, o setor elétrico sofreu uma grave crise de abastecimento que culminou em um plano de racionamento de energia elétrica. Esse acontecimento gerou uma série de questionamentos sobre os rumos que o setor elétrico estava trilhando. O reforço dos instrumentos de planejamento energético pelo governo brasileiro permitiu a recuperação de uma responsabilidade do Estado indispensável para que a expansão da oferta de energia se realize de forma satisfatória, em conformidade com o interesse público e a estratégia de desenvolvimento econômico e social. Em termos econômicos, a previsão e o controle estatístico da demanda de energia se tornaram a base da composição de preço no mercado e os erros de previsão e falhas passaram a resultar em perdas financeiras. O problema maior no estudo de modelos é otimizar os dados previstos, ou seja, minimizar os erros de previsão. Estes erros são causados pela má elaboração dos modelos ou pela grande variabilidade do processo. O objetivo deste trabalho é minimizar os erros de previsão de uma série de energia elétrica através do método de amortecimento exponencial aplicada aos pontos fora dos limites do gráfico de controle dos resíduos gerados por um modelo de previsão ARIMA. O método foi comparado a um trabalho anterior onde os dados que ficaram fora de controle foram retirados da série. Verificou-se que o resultado obtido neste trabalho apresentou um erro de previsão menor, além de manter todos os dados da série. Palavras-Chave: Previsão de Séries Temporais; Controle Estatístico de Qualidade; Energia.Abstract: In 2001, the electricity sector suffered a serious supply crisis that culminated in a plan of electricity rationing. This event generated a series of questions about the direction the electricity sector was treading. The strengthening of the tools for energy planning by the Brazilian government allowed the recovery of a state responsibility essential so that the expansion of energy to take place satisfactorily in accordance with the public interest and the strategy of economic and social development. In economic terms, forecasting and statistical control of energy demand became the basis of the composition of the market price and the forecast errors and failures began to result in financial losses. The biggest problem in the study of models is to optimize the predicted data, i.e., minimize errors of prediction. These errors are caused by poor preparation of the models or the great variability of the process. The objective is to minimize the forecast errors from a series of electricity through the exponential smoothing method applied to points outside the limits of the control chart of the waste generated for an ARIMA forecasting model. The method was compared to a last work where the data that were out of control were removed the series. It was found that the results obtained in this study showed a lesser prediction error, besides maintaining all data's series.

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Section: Previsão De Energia Elétricaunclassified

Uso Do Gráfico De Controle Para Minimizar Erros De Previsão Em Séries De Energia Elétrica

Christo¹,

Ferreira²

2013

Engevista

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“…The prediction methods are primarily divided into two categories: traditional forecasting approaches, such as time series [4], regression analysis [5], and fuzzy prediction [6], and artificially intelligent algorithms. Conventional prediction methods aiming at load forecasting poor stability and difficulty in coping with nonlinear constraints.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm

Zhang

2018

Energies

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Accurate short-term load forecasting is of momentous significance to ensure safe and economic operation of quick-change electric bus (e-bus) charging stations. In order to improve the accuracy and stability of load prediction, this paper proposes a hybrid model that combines fuzzy clustering (FC), least squares support vector machine (LSSVM), and wolf pack algorithm (WPA). On the basis of load characteristics analysis for e-bus charging stations, FC is adopted to extract samples on similar days, which can not only avoid the blindness of selecting similar days by experience, but can also overcome the adverse effects of unconventional load data caused by a sudden change of factors on training. Then, WPA with good global convergence and computational robustness is employed to optimize the parameters of LSSVM. Thus, a novel hybrid load forecasting model for quick-change e-bus charging stations is built, namely FC-WPA-LSSVM. To verify the developed model, two case studies are used for model construction and testing. The simulation test results prove that the proposed model can obtain high prediction accuracy and ideal stability.

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“…The short-term load forecasting (STLF), which is applied to horizons no more than one day ahead, can result in significant environmental and economic benefits for energy systems. For reliable and efficient operations, STLF is used when decision-making has significant impacts on the operations, such as scheduling generating capacity dispatches, demand side management, security assessments, and generator maintenance scheduling [5,[10][11][12][13][14][15][16]. Unsatisfactory STLF can cause the increase in the operational cost, equipment failures, or systems blackouts, thus resulting in a waste of resources [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting

2018

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With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel interval forecasting problem, and then further converts the interval forecasting problem into the classification forecasting problem. In addition, an indicator system influencing the electricity load is established from three dimensions, namely the load series, calendar data, and weather data. A semi-supervised feature selection algorithm is proposed to address an electricity load classification forecasting issue based on the group method of data handling (GMDH) technology. The proposed algorithm consists of three main stages: (1) training the basic classifier; (2) selectively marking the most suitable samples from the unclassified label data, and adding them to an initial training set; and (3) training the classification models on the final training set and classifying the test samples. An empirical analysis of electricity load dataset from four Chinese cities is conducted. Results show that the proposed model can address the electricity load classification forecasting problem more efficiently and effectively than the FW-Semi FS (forward semi-supervised feature selection) and GMDH-U (GMDH-based semi-supervised feature selection for customer classification) models.

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Short-Term Load Forecasting: The Similar Shape Functional Time-Series Predictor

Cited by 82 publications

References 25 publications

Uso Do Gráfico De Controle Para Minimizar Erros De Previsão Em Séries De Energia Elétrica

Uso Do Gráfico De Controle Para Minimizar Erros De Previsão Em Séries De Energia Elétrica

Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm

GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting

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