Short Term Electricity Forecasting Using Individual Smart Meter Data
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TLDR
The contribution is the proposal for accurate short term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level.About:
This article is published in Procedia Computer Science.The article was published on 2014-01-01 and is currently open access. It has received 88 citations till now. The article focuses on the topics: Smart meter & Mains electricity.read more
Citations
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Short-term residential load forecasting: Impact of calendar effects and forecast granularity
TL;DR: In this article, the authors used regression trees, neural networks, and support vector regression to forecast the day-ahead load of residential customers in a smart meter-based system, and found that the regression trees technique is significantly better than neural networks.
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Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem
Priyanka Singh,Pragya Dwivedi +1 more
TL;DR: A novel evolutionary algorithm based on follow the leader concept is developed and its performance is validated by COmparing Continuous Optimizers experimental framework on the set of 24 Black-Box Optimization Benchmarking functions and it outperformed all state-of-art algorithms in 20-D and ranked second in other dimensions.
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Recent advances in the analysis of residential electricity consumption and applications of smart meter data
TL;DR: In this article, the most recent methods and techniques for using smart meter data such as forecasting, clustering, classification and optimization have been reviewed and compared in various applications such as home and battery energy management systems and demand response strategies.
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Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting
TL;DR: An intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns and proposes unsupervised data clustering and frequent pattern mining analysis on energy timeseries, and Bayesian network prediction for energy usage forecasting.
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Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting
TL;DR: Results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results, and that using an ensemble scheme can achieve very accurate predictions.
References
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Book
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.
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Time series analysis
TL;DR: A ordered sequence of events or observations having a time component is called as a time series, and some good examples are daily opening and closing stock prices, daily humidity, temperature, pressure, annual gross domestic product of a country and so on.
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Introduction to Time Series and Forecasting.
TL;DR: A general approach to Time Series Modelling and ModeLLing with ARMA Processes, which describes the development of a Stationary Process in Terms of Infinitely Many Past Values and the Autocorrelation Function.
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Neural networks for short-term load forecasting: a review and evaluation
TL;DR: This review examines a collection of papers (published between 1991 and 1999) that report the application of NNs to short-term load forecasting, and critically evaluating the ways in which the NNs proposed in these papers were designed and tested.