Journal ArticleDOI
Electric load forecasting methods: Tools for decision making
TLDR
This article gives an overview over the various models and methods used to predict future load demands and their applications in the electricity sector.About:
This article is published in European Journal of Operational Research.The article was published on 2009-12-16. It has received 499 citations till now. The article focuses on the topics: Decision support system & Decision analysis.read more
Citations
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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
Big data driven smart energy management: From big data to big insights
Kaile Zhou,Chao Fu,Shanlin Yang +2 more
TL;DR: A systematic review of big data analytics for smart energy management from four major aspects, namely power generation side management, microgrid and renewable energy management, asset management and collaborative operation, as well as demand side management (DSM).
Journal ArticleDOI
Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques
Cheng Fan,Fu Xiao,Shengwei Wang +2 more
TL;DR: This paper presents a data mining (DM) based approach to developing ensemble models for predicting next-day energy consumption and peak power demand, with the aim of improving the prediction accuracy.
Journal ArticleDOI
A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines
TL;DR: Two important improvements to the SVR based load forecasting method are introduced, i.e., procedure for generation of model inputs and subsequent model input selection using feature selection algorithms and the use of the particle swarm global optimization based technique for the optimization of SVR hyper-parameters reduces the operator interaction.
Journal ArticleDOI
On recent advances in PV output power forecast
TL;DR: In this paper, a comprehensive and systematic review of PV output power forecast models were provided, which covers the different factors affecting PV forecast, PV output output power profile and performance matrices to evaluate the forecast model.
References
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Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Proceedings ArticleDOI
A new optimizer using particle swarm theory
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Book ChapterDOI
Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
Journal ArticleDOI
Time Series Analysis Forecasting and Control
TL;DR: This revision of a classic, seminal, and authoritative book explores the building of stochastic models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.