Journal ArticleDOI
Short-term load forecasting via ARMA model identification including non-Gaussian process considerations
Shyh-Jier Huang,Kuang-Rong Shih +1 more
TLDR
The concept of cumulant and bispectrum are embedded into the ARMA model in order to facilitate Gaussian and non-Gaussian process considerations, and with embodiment of a Gaussianity verification procedure, the forecasted model is identified more appropriately.Abstract:
In this paper, the short-term load forecast by use of autoregressive moving average (ARMA) model including non-Gaussian process considerations is proposed. In the proposed method, the concept of cumulant and bispectrum are embedded into the ARMA model in order to facilitate Gaussian and non-Gaussian process. With embodiment of a Gaussianity verification procedure, the forecasted model is identified more appropriately. Therefore, the performance of ARMA model is better ensured, improving the load forecast accuracy significantly. The proposed method has been applied on a practical system and the results are compared with other published techniques.read more
Citations
More filters
Book
Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach
TL;DR: In this paper, the authors present a case study of the electricity market in the UK and Australia, showing that electricity prices in both countries are correlated with the number of customers and the amount of electricity consumed.
Journal ArticleDOI
A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings
TL;DR: In this article, the authors provide a comprehensive and systematic literature review of Artificial Intelligence based short-term load forecasting techniques and provide the major objective of this study is to review, identify, evaluate and analyze the performance of artificial Intelligence based load forecast models and research gaps.
Journal ArticleDOI
A comparison of univariate methods for forecasting electricity demand up to a day ahead
TL;DR: In this article, the authors compared the performance of six univariate methods for short-term electricity demand forecasting for lead times up to a day ahead and concluded that simpler and more robust methods can outperform more complex alternatives.
Journal ArticleDOI
Electric load forecasting methods: Tools for decision making
TL;DR: This article gives an overview over the various models and methods used to predict future load demands and their applications in the electricity sector.
Journal ArticleDOI
Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey
Damminda Alahakoon,Xinghuo Yu +1 more
TL;DR: A comprehensive survey of smart electricity meters and their utilization is presented focusing on key aspects of the metering process, different stakeholder interests, and the technologies used to satisfy stakeholder interest.
References
More filters
Book ChapterDOI
Time Series Analysis
TL;DR: This paper provides a concise overview of time series analysis in the time and frequency domains with lots of references for further reading.
Journal ArticleDOI
Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications
TL;DR: A compendium of recent theoretical results associated with using higher-order statistics in signal processing and system theory is provided, and the utility of applying higher- order statistics to practical problems is demonstrated.
Journal ArticleDOI
Electric load forecasting using an artificial neural network
TL;DR: In this article, an artificial neural network (ANN) approach is presented for electric load forecasting, which is used to learn the relationship among past, current and future temperatures and loads.
Journal ArticleDOI
Short-term load forecasting
TL;DR: In this paper, the authors discuss the state of the art in short-term load forecasting (STLF), that is, the prediction of the system load over an interval ranging from one hour to one week.
Journal ArticleDOI
Signal processing with higher-order spectra
TL;DR: The strengths and limitations of correlation-based signal processing methods, with emphasis on the bispectrum and trispectrum, and the applications of higher-order spectra in signal processing are discussed.