One hour ahead price forecast of Ontario electricity market by using ANN

Sahay, Kishan Bhushan

doi:10.1109/energyeconomics.2015.7235102

Cited by 13 publications

(8 citation statements)

References 5 publications

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“…Artificial neural networks are outstanding for short-term forecasting, and they are efficiently applicable for electricity markets [161], being more accurate and robust than autoregressive (AR) models. The research [48] uses artificial neural network models to display the strong impact of electricity price on the trend load and MCP.…”

Section: Discussion Of Forecasting Models On Electricity Marketsmentioning

confidence: 99%

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Acaroğlu

Márquez

2021

Energies

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Forecasting the electricity price and load has been a critical area of concern for researchers over the last two decades. There has been a significant economic impact on producers and consumers. Various techniques and methods of forecasting have been developed. The motivation of this paper is to present a comprehensive review on electricity market price and load forecasting, while observing the scientific approaches and techniques based on wind energy. As a methodology, this review follows the historical and structural development of electricity markets, price, and load forecasting methods, and recent trends in wind energy generation, transmission, and consumption. As wind power prediction depends on wind speed, precipitation, temperature, etc., this may have some inauspicious effects on the market operations. The improvements of the forecasting methods in this market are necessary and attract market participants as well as decision makers. To this end, this research shows the main variables of developing electricity markets through wind energy. Findings are discussed and compared with each other via quantitative and qualitative analysis. The results reveal that the complexity of forecasting electricity markets’ price and load depends on the increasing number of employed variables as input for better accuracy, and the trend in methodologies varies between the economic and engineering approach. Findings are specifically gathered and summarized based on researches in the conclusions.

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Section: Discussion Of Forecasting Models On Electricity Marketsmentioning

confidence: 99%

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Acaroğlu

Márquez

2021

Energies

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“…In the New York electricity market, in 1-hour forecasts, Cheng et al (2020) obtained MAPE values between 5.35-7.64 with Backpropagation, CNN, andLSTM, andHuang et al (2020) obtained MAPE values between 5.23-6.55 with LSTM and CNN. In the Ontario market, Sahay (2015) obtained deviation values between 9.43%-41.97% in one-hour forecasts with ANN, and Jahangir et al (2020) obtained deviation values between 8.31%-37.97% with CNN and LSTM.…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity Price Forecasting in Turkish Day-Ahead Market via Deep Learning Techniques

Arifoğlu

Kandemir

2022

Mehmet Akif Ersoy Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi

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Day-Ahead Market offers electricity market participants the opportunity to trade electricity one day ahead of real-time. For each hour, a separate Market Clearing Price is created in Day-Ahead Market. This study aims to predict the hourly Market Clearing Price using deep learning techniques. In this context, 24-hour Market Clearing Prices were forecasted with MLP, CNN, LSTM, and GRU. LSTM had the best average forecasting performance with an 8.15 MAPE value, according to the results obtained. MLP followed the LSTM with 8.44 MAPE, GRU with 8.72 MAPE, and CNN with 9.27 MAPE. In the study, the provinces where the power plants producing with renewable resources are dense were selected for meteorological variables. It is expected that the trend towards electricity generation with renewable resources will increase in the future. In this context, it is thought important for market participants to consider the factors that may affect the production with these resources in the electricity price forecasting.

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“…Artificial intelligence (AI) methods such as artificial neural network (ANN) and support vector machine (SVM) do not require high stability and these models can obtain accurate and stable predictions through the training data [3]. The development of NN models have been reported by many researchers [3][4][5][6][7][8][9][10]. One of the methodologies of NNs is deep neural network, which has gained popularity rapidly and has been applied to predict electricity prices [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…One of the methodologies of NNs is deep neural network, which has gained popularity rapidly and has been applied to predict electricity prices [4], [5]. In [6], the Levenberg-Marquardt backpropagation algorithm was applied on the Ontario energy market. Meanwhile, in [7], recurrent neural networks and excitable dynamics were developed and evaluated on the Ontario, New South Wales, Spain, and California energy markets.…”

Section: Introductionmentioning

confidence: 99%

Next-Hour Electricity Price Forecasting Using Least Squares Support Vector Machine and Genetic Algorithm

Razak

Abidin

Yap

et al. 2022

AEJ

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Predicting the price of electricity is crucial for the operation of power systems. Short-term electricity price forecasting deals with forecasts from an hour to a day ahead. Hourly-ahead forecasts offer expected prices to market participants before operation hours. This is especially useful for effective bidding strategies where the bidding amount can be reviewed or changed before the operation hours. Nevertheless, many existing models have relatively low prediction accuracy. Furthermore, single prediction models are typically less accurate for different scenarios. Thus, a hybrid model comprising least squares support vector machine (LSSVM) and genetic algorithm (GA) was developed in this work to predict electricity prices with higher accuracy. This model was tested on the Ontario electricity market. The inputs, which were the hourly Ontario electricity price (HOEP) and demand for the previous seven days, as well as 1-h pre-dispatch price (PDP), were optimized by GA to prevent losing potentially important inputs. At the same time, the LSSVM parameters were optimized by GA to obtain accurate forecasts. The hybrid LSSVM-GA model was shown to produce an average mean absolute percentage error (MAPE) of 8.13% and the structure of this model is less complex compared with other models developed in previous studies. This is due to the fact that only two algorithms were used (LSSVM and GA), with the load and HOEP for the week preceding the forecasting hour as the inputs. Based on the results, it is concluded that the proposed hybrid algorithm is a promising alternative to produce good electricity price forecasts.

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One hour ahead price forecast of Ontario electricity market by using ANN

Cited by 13 publications

References 5 publications

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Electricity Price Forecasting in Turkish Day-Ahead Market via Deep Learning Techniques

Next-Hour Electricity Price Forecasting Using Least Squares Support Vector Machine and Genetic Algorithm

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