Electricity Market Price Forecasting Based on Weighted Nearest Neighbors Techniques

Lora, Alicia Troncoso; Santos, José Cristóbal Riquelme; Gómez-Expósito, Antonio; Ramos, José Luis Martínez

doi:10.1109/tpwrs.2007.901670

Cited by 199 publications

(73 citation statements)

References 17 publications

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“…Also, for the sake of a fair comparison, the same test weeks as in [13][14][15][16][17][18][19][20][21][22] Table 2 presents the values for the criterions to evaluate the accuracy of the HPA approach in forecasting electricity prices. The first column indicates the week, the second column presents the MAPE in percent, the third column presents the square root of the SSE, and the fourth column presents the SDE.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The soft computing techniques include neural networks (NN) [16], neural networks combined with wavelet transform (NNWT) [17], fuzzy neural networks (FNN) [18], weighted nearest neighbors (WNN) [19], adaptive wavelet neural network (AWNN) [20], hybrid intelligent system (HIS) [21], cascaded neuro-evolutionary algorithm (CNEA) [22], and other hybrid approaches [23,24]. Usually, an inputoutput mapping is learned from historical examples, thus there is no need to model the system.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed approach is based on the combination of particle swarm optimization (PSO) and adaptive-network based fuzzy inference system (ANFIS). Our HPA (hybrid PSO-ANFIS) approach is examined on the electricity market of mainland Spain, commonly used as the test case in several price forecasting papers [13][14][15][16][17][18][19][20][21][22]. It has been concluded that the Spanish market has a hard nonlinear behaviour and time variant functional relationship [14,18].…”

Section: Introductionmentioning

confidence: 99%

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Short-term electricity prices forecasting in a competitive market by a hybrid PSO–ANFIS approach

Pousinho

Mendes

Catalão

2012

International Journal of Electrical Power & Energy Systems

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In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of one week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Short-term electricity prices forecasting in a competitive market by a hybrid PSO–ANFIS approach

Pousinho

Mendes

Catalão

2012

International Journal of Electrical Power & Energy Systems

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“…In [11], Pattern Sequence-based Forecasting (PSF) was reported to have outperformed other contemporary works, namely ARIMA, naive Bayes, ANN (this ANN implementation is distinct from our previous work (ANN-only) [12] because of different feature engineering approaches), WNN (Weighted Nearest Neighbor) [54], the Structural model (STR) [55] and other mixed models. As testing was performed on the 2004-2006 data from all three markets in the PSF paper, we also perform the testing on same data and achieve the better results shown in Tables 2-4.…”

Section: Proposed Methods Vs Psfmentioning

confidence: 99%

Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

Neupane¹,

Woon²,

Aung³

2016

Preprint

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Day-ahead forecasting of electricity prices is important in deregulated electricity markets for all the stakeholders: energy wholesalers, traders, retailers, and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participate in predicting the price for each hour of a day. We propose two different strategies, namely, Fixed Weight Method (FWM) and Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. The proposed ensemble model offers better results than both the Pattern Sequence-based Forecasting (PSF) method and our own previous work using Artificial Neural Networks (ANN) alone do on the datasets for New York, Australian, and Spanish electricity markets.

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“…Nogales and Conejo proposed transfer function models that outperformed ARIMA and ANN [10]. Lora et al proposed a weighted nearest neighbors methodology that outperforms ANN, neuro-fuzzy system and GARCH models [11].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model

et al. 2017

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Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 e/MWh for day-ahead market and a maximum value of 2.53 e/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions.

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Electricity Market Price Forecasting Based on Weighted Nearest Neighbors Techniques

Cited by 199 publications

References 17 publications

Short-term electricity prices forecasting in a competitive market by a hybrid PSO–ANFIS approach

Short-term electricity prices forecasting in a competitive market by a hybrid PSO–ANFIS approach

Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model

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