Journal ArticleDOI
Day-Ahead Electricity Price Forecasting in a Grid Environment
TLDR
In this article, a combination of fuzzy inference system (FIS), least squares estimation (LSE), and the combination of FIS and LSE are proposed for electricity price forecasting in locational marginal pricing spot markets.Abstract:
Accurate electricity price forecasting is critical to market participants in wholesale electricity markets. Market participants rely on price forecasts to decide their bidding strategies, allocate assets, negotiate bilateral contracts, hedge risks, and plan facility investments. Market operators can also use electricity price forecasts to predict market power indexes for the purpose of monitoring participants' behaviors. Various forecasting techniques are applied to different time horizons for electricity price forecasting in locational marginal pricing (LMP) spot markets. Available correlated data also have to be selected to improve the short-term forecasting performance. In this paper, fuzzy inference system (FIS), least-squares estimation (LSE), and the combination of FIS and LSE are proposed. Based on extensive testing with various techniques, LSE provides the most accurate results, and FIS, which is also highly accurate, provides transparency and interpretabilityread more
Citations
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Journal ArticleDOI
Load Scheduling and Dispatch for Aggregators of Plug-In Electric Vehicles
TL;DR: A minimum-cost load scheduling algorithm is designed, which determines the purchase of energy in the day-ahead market based on the forecast electricity price and PEV power demands, and a dynamic dispatch algorithm is developed, used for distributing the purchased energy to PEVs on the operating day.
Journal ArticleDOI
An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets
TL;DR: In this paper, an adaptive wavelet neural network (AWNN) was proposed for short-term price forecasting in the electricity markets, where a commonly used Mexican hat wavelet has been chosen as the activation function for hidden-layer neurons of feed-forward neural network.
Journal ArticleDOI
Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm
Nima Amjady,Farshid Keynia +1 more
TL;DR: In this article, a hybrid forecast method is proposed for this purpose, composed of wavelet transform (WT), neural network (NN) and evolutionary algorithm (EA), which can efficiently decompose the time series into its components.
Journal ArticleDOI
Day-Ahead Price Forecasting of Electricity Markets by Mutual Information Technique and Cascaded Neuro-Evolutionary Algorithm
Nima Amjady,Farshid Keynia +1 more
TL;DR: In this paper, a combination of a feature selection technique and cascaded neuro-evolutionary algorithm (CNEA) is proposed for the purpose of price forecast in a competitive electricity market.
Journal ArticleDOI
Electricity Price Forecasting With Extreme Learning Machine and Bootstrapping
TL;DR: In this paper, a fast electricity market price forecast method is proposed based on a recently emerged learning method for single hidden layer feed-forward neural networks, the extreme learning machine (ELM), to overcome these drawbacks.
References
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Journal ArticleDOI
Generating fuzzy rules by learning from examples
Li-Xin Wang,Jerry M. Mendel +1 more
TL;DR: The mapping is proved to be capable of approximating any real continuous function on a compact set to arbitrary accuracy and applications to truck backer-upper control and time series prediction problems are presented.
Journal ArticleDOI
ARIMA models to predict next-day electricity prices
TL;DR: In this article, a method to predict next-day electricity prices based on the ARIMA methodology is presented, which is used to analyze time series and have been mainly used for load forecasting, due to their accuracy and mathematical soundness.
Journal ArticleDOI
ARIMA Models to Predict Next-Day Electricity Prices
Journal ArticleDOI
Day-ahead electricity price forecasting using the wavelet transform and ARIMA models
TL;DR: A novel technique to forecast day-ahead electricity prices based on the wavelet transform and ARIMA models is proposed, where the historical and usually ill-behaved price series is decomposed using the wavelets to reconstruct the future behavior of the price series and therefore to forecast prices.
Journal ArticleDOI
Forecasting next-day electricity prices by time series models
TL;DR: In this article, the authors provide two highly accurate yet efficient price forecasting tools based on time series analysis: dynamic regression and transfer function models, which are explained and checked against each other.