Mid-term electricity market clearing price forecasting utilizing hybrid support vector machine and auto-regressive moving average with external input

Yan, Xing; Chowdhury, N.

doi:10.1016/j.ijepes.2014.05.037

Cited by 41 publications

(18 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…One forecast strategy is a new two-stage feature selection (FS) algorithm, which is proposed by Keynia [4] and is based on the mutual information (MI) criterion; it selects representative features of the composite neural network (CNN) among feature candidates. Yan et al [5,6] applied a multiple support vector machine (SVM) network cascaded with a multi-layer feedforward (MLF) network for forecasting locational marginal prices (LMPs). By combining statistical techniques for pre-processing data and a multi-layer neural network, a dynamic hybrid model was proposed by Cerjan et al [38] for forecasting electricity prices and price spike detection.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection

2016

View full text Add to dashboard Cite

Abstract:The day-ahead electricity market is closely related to other commodity markets such as the fuel and emission markets and is increasingly playing a significant role in human life. Thus, in the electricity markets, accurate electricity price forecasting plays significant role for power producers and consumers. Although many studies developing and proposing highly accurate forecasting models exist in the literature, there have been few investigations on improving the forecasting effectiveness of electricity price from the perspective of reducing the volatility of data with satisfactory accuracy. Based on reducing the volatility of the electricity price and the forecasting nature of the radial basis function network (RBFN), this paper successfully develops a two-stage model to forecast the day-ahead electricity price, of which the first stage is particle swarm optimization (PSO)-core mapping (CM) with self-organizing-map and fuzzy set (PCMwSF), and the second stage is selection rule (SR). The PCMwSF stage applies CM, fuzzy set and optimized weights to obtain the future price, and the SR stage is inspired by the forecasting nature of RBFN and effectively selects the best forecast during the test period. The proposed model, i.e., CM-PCMwSF-SR, not only overcomes the difficulty of reducing the high volatility of the electricity price but also leads to a superior forecasting effectiveness than benchmarks.

show abstract

Section: Introductionmentioning

confidence: 99%

A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection

2016

View full text Add to dashboard Cite

show abstract

“…The testing data set includes 46 days of crane operations with the total number of hours equal to 1104 from two different electrified RTG cranes. The MAPE, RMSE and MAE values are used to measure the model performance, see (11) to (13). In this section, the forecast performance for all models are presented and then the model that performed best is further analysed.…”

Section: Resultsmentioning

confidence: 99%

“…2) Artificial intelligence methods: such as artificial neural networks (ANN) [9] and support vector machine (SVM) [10]. 3) Hybrid forecast system: for instance, ARMAX-SVM [11] and regime switching models [12].…”

Section: Introductionmentioning

confidence: 99%

Day-ahead industrial load forecasting for electric RTG cranes

Alasali

Haben

Becerra

et al. 2018

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

Given the increase in international trading and the significant energy and environmental challenges in ports around the world, there is a need for a greater understanding of the energy demand behaviour at ports. The move towards electrified rubber-tyred gantry (RTG) cranes is expected to reduce gas emissions and increase energy savings compared to diesel RTG cranes but it will increase electrical energy demand. Electrical load forecasting is a key tool for understanding the energy demand which is usually applied to data with strong regularities and seasonal patterns. However, the highly volatile and stochastic behaviour of the RTG crane demand creates a substantial prediction challenge. This paper is one of the first extensive investigations into short term load forecasts for electrified RTG crane demand. Options for model inputs are investigated depending on extensive data and correlation analysis. The effect of estimation accuracy of exogenous variables on the forecast accuracy is investigated as well. The models are tested on two different RTG crane data sets that were collected from the Port of Felixstowe in the UK. The results reveal the effectiveness of the forecast models when the estimation of the number of crane moves and container gross weight are accurate.

show abstract

“…To develop a more reliable forecasting model, Kavousi-Fard [14] developed a new hybrid model combining ARIMA and Support Vector Regression (SVR) with the cuckoo search algorithm method and applied it to examine Fars Electrical Power Company's empirical peak load data in Iran. Yan [15] used a novel hybrid mid-term electricity market clearing price (MCP) forecasting model that combined both the SVM and Auto Regressive Moving Average with External input (ARMAX) models and demonstrated its improved forecasting accuracy. Using an integrated approach combined an emotional learning based fuzzy inference system and ANN with an adaptive neuro-fuzzy inference system, Azadeh [16] proposed a hybrid forecasting model and used it to forecast natural gas consumption forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Hybrid Method for China’s Energy Supply Security Forecasting Based on ARIMA and XGBoost

Пин

Zhang

2018

Energies

View full text Add to dashboard Cite

Abstract:Energy supply security is a significant part of China's security, directly influencing national security and economic and social sustainability. To ensure both China's present and the future energy supply, it is essential to evaluate and forecast the energy supply level. However, forecasting the energy supply security level is difficult because energy supply security is dynamic, many factors affect it and there is a lack of accurate and comprehensive data. Therefore, based on previous studies and according to the characteristics of energy supply and the social development of China, first, the authors apply a comprehensive evaluation method to quantify the energy supply security. Second, based on the ARIMA-XGBoost hybrid model, the authors create two novel approaches for forecasting the energy supply security level of China. The authors find that: (1) energy supply security is dynamic, and green development has become the theme of China's energy development. The energy industry urgently needs to provide more high-quality ecological energy products to meet the people's desire for a beautiful ecological environment; (2) since the mean absolute percentage errors are below 4.5% when forecasting the energy supply security indicators, the ARIMA-XGBoost hybrid model is more accurate for forecasting China's energy supply security level and (3) the security level of China's energy supply has developed periodic features; the ESSI can improve by about 0.2 every five years, but, due to the low starting point and multiple types of constraints, it is difficult to reach the safety level in a short time.

show abstract

Mid-term electricity market clearing price forecasting utilizing hybrid support vector machine and auto-regressive moving average with external input

Cited by 41 publications

References 34 publications

A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection

A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection

Day-ahead industrial load forecasting for electric RTG cranes

A New Hybrid Method for China’s Energy Supply Security Forecasting Based on ARIMA and XGBoost

Contact Info

Product

Resources

About