Day ahead electricity price forecast by NARX model with LASSO based features selection

2019 ◽  
Author(s):  
Alessandro Brusaferri ◽  
Lorenzo Fagiano ◽  
Matteo Matteucci ◽  
Andrea Vitali
Keyword(s):  
Electricity Price ◽  
Features Selection ◽  
Price Forecast ◽  
Narx Model

scholarly journals Electricity price forecast model based on machine learning

2021 ◽  
Vol 7 (2) ◽  
pp. 13-26
Author(s):  
Petrică RĂDAN ◽  
◽  
Florin-Emilian CIAUȘIU ◽  
Bogdan-Ștefan ACHIM ◽  
Andrei STAN ◽  
...  
Keyword(s):  
Machine Learning ◽  
Forecast Model ◽  
Electricity Price ◽  
Price Forecast ◽  
Model Based

Bio-Inspired System for Electricity Price Forecast in the Brazilian Market

2020 ◽  
Author(s):  
Lidio Mauro Lima de Campos ◽  
Jherson Haryson Almeida Pereira ◽  
Danilo Souza Duarte ◽  
Roberto Celio Limao de Oliveira
Keyword(s):  
Electricity Price ◽  
Price Forecast

scholarly journals The Financial Effect of the Electricity Price Forecasts’ Inaccuracy on a Hydro-Based Generation Company

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2093 ◽  
Author(s):  
Umut Ugurlu ◽  
Oktay Tas ◽  
Aycan Kaya ◽  
Ilkay Oksuz
Keyword(s):  
Short Term Memory ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Economic Loss ◽  
Electricity Price ◽  
Mixed Integer ◽  
Forecast Performance ◽  
Price Forecast ◽  
Ex Post ◽  
Loss Index

Electricity price forecasting has a paramount effect on generation companies (GenCos) due to the scheduling of the electricity generation scheme according to electricity price forecasts. Inaccurate electricity price forecasts could cause important loss of profits to the suppliers. In this paper, the financial effect of inaccurate electricity price forecasts on a hydro-based GenCo is examined. Electricity price forecasts of five individual and four hybrid forecast models and the ex-post actual prices are used to schedule the hydro-based GenCo using Mixed Integer Linear Programming (MILP). The financial effect measures of profit loss, Economic Loss Index (ELI) and Price Forecast Disadvantage Index (PFDI), as well as Mean Absolute Error (MAE) of the models are used for comparison of the data from 24 weeks of the year. According to the results, a hybrid model, 50% Artificial Neural Network (ANN)–50% Long Short Term Memory (LSTM), has the best performance in terms of financial effect. Furthermore, the forecast performance evaluation methods, such as Mean Absolute Error (MAE), are not necessarily coherent with inaccurate electricity price forecasts’ financial effect measures.

Developed Machine Learning Technology and its Application on Electric Power System

2011 ◽  
Vol 217-218 ◽  
pp. 1289-1292
Author(s):  
Hua Zheng ◽  
Li Xie ◽  
Li Zi Zhang
Keyword(s):  
Electricity Market ◽  
Influence Factors ◽  
Forecast Model ◽  
Electric Power System ◽  
Electricity Price ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Learning Technology ◽  
Feature Extraction Method ◽  
Price Forecast

There is a general consensus that the movement of electricity price is crucial for electricity market. As a practical tool to estimate the future prices, electricity price forecast is of great importance and use for the operations of market participants. So a hybrid forecast model is proposed in this paper that integrates independent component analysis (ICA) with least squares support vector machines (LS-SVM). First, a novel feature extraction method of price influence factors is proposed based on ICA, which aims at mining the latent source-features by using the higher-ordered statistical characteristics. After that, nonlinear regression modeling of electricity price and its extracted features is accomplished by LS-SVM with more efficient training and forecasting. Finally, Californian market data are employed to test the proposed approach.

scholarly journals Electricity Price Forecast Using Combined Models with Adaptive Weights Selected and Errors Calibrated by Hidden Markov Model

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Da Liu ◽  
Yanan Wei ◽  
Shuxia Yang ◽  
Zhitao Guan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Electricity Market ◽  
Hidden Markov ◽  
Electricity Price ◽  
Support Vector ◽  
Price Forecast ◽  
Adaptive Weights ◽  
Generalized Regression Neural Networks ◽  
The Individual

A combined forecast with weights adaptively selected and errors calibrated by Hidden Markov model (HMM) is proposed to model the day-ahead electricity price. Firstly several single models were built to forecast the electricity price separately. Then the validation errors from every individual model were transformed into two discrete sequences: an emission sequence and a state sequence to build the HMM, obtaining a transmission matrix and an emission matrix, representing the forecasting ability state of the individual models. The combining weights of the individual models were decided by the state transmission matrixes in HMM and the best predict sample ratio of each individual among all the models in the validation set. The individual forecasts were averaged to get the combining forecast with the weights obtained above. The residuals of combining forecast were calibrated by the possible error calculated by the emission matrix of HMM. A case study of day-ahead electricity market of Pennsylvania-New Jersey-Maryland (PJM), USA, suggests that the proposed method outperforms individual techniques of price forecasting, such as support vector machine (SVM), generalized regression neural networks (GRNN), day-ahead modeling, and self-organized map (SOM) similar days modeling.

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