Monthly energy forecasting using decomposition method with application of seasonal ARIMA

Damrongkulkamjorn, P.; Churueang, P.

doi:10.1109/ipec.2005.206911

Cited by 24 publications

(14 citation statements)

References 4 publications

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“…where Y t is the signal, T t is the component of trend, S t the component of seasonality, and e t the remaining irregular component assumed to be random variables with zero mean and constant variance at time t [20,21]. In our study, Y t refers to the time series of monthly PR over the observed period of 60 months; hence, t ranges from 1 to 60 (n equals to 60).…”

Section: Estimation Of Degradation Ratementioning

confidence: 99%

Performance Analysis of a Grid-Connected Upgraded Metallurgical Grade Silicon Photovoltaic System

et al. 2016

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Because of their low cost, photovoltaic (PV) cells made from upgraded metallurgical grade silicon (UMG-Si) are a promising alternative to conventional solar grade silicon-based PV cells. This study investigates the outdoor performance of a 1.26 kW grid-connected UMG-Si PV system over five years, reporting the energy yields and performance ratio and estimating the long-term performance degradation rate. To make this investigation more meaningful, the performance of a mono-Si PV system installed at the same place and studied during the same period of time is presented for reference. Furthermore, this study systematizes and rationalizes the necessity of a data selection and filtering process to improve the accuracy of degradation rate estimation. The impact of plane-of-array irradiation threshold for data filtering on performance ratio and degradation rate is also studied. The UMG-Si PV system's monthly performance ratio after data filtering ranged from 84% to 93% over the observation period. The annual degradation rate was 0.44% derived from time series of monthly performance ratio using the classical decomposition method. A comparison of performance ratio and degradation rate to conventional crystalline silicon-based PV systems suggests that performance of the UMG-Si PV system is comparable to that of conventional systems.

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Section: Estimation Of Degradation Ratementioning

confidence: 99%

Performance Analysis of a Grid-Connected Upgraded Metallurgical Grade Silicon Photovoltaic System

et al. 2016

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“…Hence various KunalLohia forecasting models such as ARIMA [5], surface fitting [6], neural networks [7], extreme learning machine (ELM) [8] and online sequential extreme learning machine (OSELM) [9] have been applied for the forecasting of wind power to mitigate the effects of integrating wind power. Wind power prediction models are generally classified into two categories which are physical model and statistical model [2].…”

Section: Introductionmentioning

confidence: 99%

Comparative study of power forecasting methods for wind farms

Lohia

Garg

Shrivastava

et al. 2015

2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015]

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This paper presents a comparative study of various forecasting models for wind power. With the growing wind power usage in the power system, wind power forecasting is very much needed to help the power system in unit commitment, economic scheduling and reserve allocation problems. Wind power forecasting using autoregressive integrated moving average model, surface fitting model, neural networks, extreme learning machine and online sequential extreme learning machine is carried out in this paper. The performance characteristics of different forecasting models have been compared by applying different measure of errors such as bias, mean absolute error, root mean square error and standard deviation. The effectiveness of online sequential extreme learning machine is evaluated on the given wind power data and the results demonstrate that the online sequential extreme learning machine performance characteristic is better as compared with the other forecasting models.

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“…é o resultado da série no tempo é a componente de tendência no tempo é a componente de sazonalidade no tempo é a componente residual (erro) no tempo O modelo multiplicativo assume que a série temporal pode ser descrita como em (8) [9], [37], [38].…”

Section: Um Dos Modelos Tradicionalmente Utilizados é O Modelo De Decunclassified

“…A componente de tendência indica as mudanças nos dados no longo prazo, enquanto a componente de sazonalidade indica mudanças que dependem de alguma sazonalidade, geralmente baseadas no clima ou época do ano [9], neste caso representado intervalos característicos dentro do período de análise de curto • Data e hora da coleta;…”

Section: Um Dos Modelos Tradicionalmente Utilizados é O Modelo De Decunclassified

“…Publicações nos últimos 10 anos Fonte: Web of Science -Pesquisa por termos + Rede Neural Observa-se que existe um crescimento quase que constante do tema no período, sendo que aproximadamente metade das pesquisas estão de alguma forma, relacionadas com a utilização de redes neurais artificiais, apesar deste ser apenas um dos métodos possíveis para solução do problema.O trabalho desenvolvido por[9] apresenta uma abordagem para previsão do consumo mensal de energia aplicando o método auto-regressivo Integrado de Média Móvel (Autoregressive Integrated Moving Average -ARIMA) para técnica clássica de decomposição de séries temporais.Nesse artigo, o método da decomposição multiplicativa é utilizado para decompor a série temporal em uma componente relacionada à "tendência-ciclo" e outra relacionada à sazonalidade. A componente de "tendência-ciclo" é utilizada para, através do método ARIMA agora denominado SARIMA, encontrar o melhor modelo que descreve os dados.…”

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Desenvolvimento de uma nova metodologia para previsão do consumo de energia elétrica de curto prazo utilizando redes neurais artificiais e decomposição de séries temporais.

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Desenvolvimento de uma nova metodologia para previsão do consumo de energia elétrica de curto prazo utilizando redes neurais artificiais e decomposição de séries temporais SÃO PAULO 2020 Dedico este trabalho com todo carinho aos meus pais, Haroldo P. Amaral e Cleide Moretti, que sempre estiveram presentes e me apoiaram na concretização desta conquista. Dedico também à querida Prof. a Dra. Maria Goretti Zago (in memoriam) que me apoiou e incentivou em todas as etapas da minha caminhada acadêmica. Palavras-chave: Previsão do consumo de energia elétrica. Redes neurais artificiais. Decomposição de série temporal. Redes inteligentes. Eficiência energética. AbstractResidential consumers are increasingly gaining access to new measurement technologies and remote operation with the spread of smart grids. These opportunities have brought improvements in the quality of service and the establishment of new levels of financial savings. Recent studies have shown that a significant increase in energy efficiency is related to changes in consumption habits induced by new technologies that provide access to information on the quality and quantity of energy consumed. In this context, short-term energy consumption forecasting is an awareness tool based on providing useful information ready to assist in the pursuit of the most efficient energy consumption and saving patterns.This thesis presents the development of a new methodology for the prediction of residential energy consumption, using artificial networks and time series decomposition. Several tests were performed to obtain the best configuration for the electricity consumption forecasting problem. The tests were also performed compared to the available combined methods, allowing an evaluation of the obtained results. The results demonstrated by this methodology reached a low average percentage error and a low average absolute percentage error in the forecast horizon, which are the most used metrics in this type of study.

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Monthly energy forecasting using decomposition method with application of seasonal ARIMA

Cited by 24 publications

References 4 publications

Performance Analysis of a Grid-Connected Upgraded Metallurgical Grade Silicon Photovoltaic System

Performance Analysis of a Grid-Connected Upgraded Metallurgical Grade Silicon Photovoltaic System

Comparative study of power forecasting methods for wind farms

Desenvolvimento de uma nova metodologia para previsão do consumo de energia elétrica de curto prazo utilizando redes neurais artificiais e decomposição de séries temporais.

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