Forecasting loads and prices in competitive power markets

By D. W. Bunn. Proceedings of the IEEE, 2000

504 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/571fc590bcf14034f91fcb0918692771af761dfa

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Citations in the corpus

Short-Term Forecasting of Anomalous Load Using Rule-Based Triple Seasonal Methods (2013, background): “A slight increase in the load forecast error leads to a considerable increase in operating costs for an electric utility [1].”
Probabilistic forecasts of the magnitude and timing of peak electricity demand (2005, background): “Having access to accurate forecasts of electricity demand provides the manager with the ability to reduce risk and minimize costs.”
A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method (2007, methodology): “For example, time-series [7], ARIMA model [11], GARCH model [2], artiﬁcial neural networks (ANN) [5], [10], [12]–[14], wavelet transform and ARIMA models [23], agents-based simulations [16], input-output hidden Markov models (IOHMM) [15], fuzzy neural network [24], etc.”
Constructing bidding curves for a price-taking retailer in the norwegian electricity market (2005, unknown): “See [6] for a load forecasting model.”
Feature extraction via multiresolution analysis for short-term load forecasting (2005, unknown): “I. INTRODUCTION ARTIFICIAL neural networks (NNs) have been success-fully applied to Short-Term Load Forecasting (STLF) [1]–[4].”
Forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal exponential smoothing (2009, background): “Keywords: Forecasting; Time Series; Exponential smoothing; Electricity load; Call centre arrivals”
Triple seasonal methods for short-term electricity demand forecasting (2010, methodology): “Three such methods, shown to be competitive in recent empirical studies, are double seasonal ARMA, an adaptation of Holt-Winters exponential smoothing for double seasonality, and another, recently proposed, exponential smoothing method.”
Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models (2008, methodology): “, 2006), regime-switching regressions with fundamental variables (Karakatsani and Bunn, 2008) and mean-reverting jump diffusions (Knittel and Roberts, 2005). The objective of this paper is to further explore the usefulness of time series models for STPF in electricity markets. There are two main con”
Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach (2006, background): “Bunn (2006) reviews the British experience of electricity liberalization.”
Electricity price forecasting in deregulated markets: A review and evaluation (2009, background): “In the current deregulated scenario, the forecasting of electricity demand and price has emerged as one of the major research fields in electrical engineering [2].”
Demand-side view of electricity markets (2003, unknown): “generation factors [21], [22].”
Short-Term Load Forecasting Methods: An Evaluation Based on European Data (2007, background): “More generally, error in predicting electricity load has significant cost implications for companies operating in competitive power markets [5].”
Optimal Bidding Strategies for Hydro-Electric Producers: A Literature Survey (2014, background): “This result allows producers to use fundamental modeling practices for price forecasting [19], but does not hold when producers’ bids impact prices.”
Day-ahead electricity price forecasting using the wavelet transform and ARIMA models (2005, methodology): “…to forecast day-ahead prices include ARIMA models [1] and [3], dynamic regression models [2], other time series techniques [4] and [5], neural network procedures [6]–[13], wavelet transform models [14] and [15], heuristics [16], Bayesian techniques [17], and simulations and others [18]–[20].”