Electricity market prices are highly volatile, highly frequent, non-linear, and non-stationary which makes forecasting very complicated. Although accurate forecasting plays a crucial role in the electricity market for traders, retailers, large consumers as well as generation companies in terms of economic efficiency and power systems safety. Hence, this article proposes a new forecasting approach for medium-term electricity market prices based on an extreme learning machine-autoencoder (ELM-AE). The main idea behind this is to use trained weights for hidden layers instead of randomly generated weights. The input hidden layer weights are obtained by solving a network with the same input outputs by the autoencoder method. Then, the obtained output weights are used again as the input weights for a new ELM network. To do so, a data-set is created using input data, where the ahead 24 hours are forecasted based on the previous 168 data. The simulations have been performed on New York Independent System Operator prices and compared with the classic ELM demonstrating the high accuracy of the proposed method in both training and testing.
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页码:7214 / 7223
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[1]
Afrasiabi M., 2022, Electric Power Syst. Res., V213
机构:
North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R ChinaNorth China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
Gao, Tian
Niu, Dongxiao
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North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R ChinaNorth China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
Niu, Dongxiao
Ji, Zhengsen
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机构:
North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R ChinaNorth China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
Ji, Zhengsen
Sun, Lijie
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机构:
North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R ChinaNorth China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
机构:
North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R ChinaNorth China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
Gao, Tian
Niu, Dongxiao
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h-index: 0
机构:
North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R ChinaNorth China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
Niu, Dongxiao
Ji, Zhengsen
论文数: 0引用数: 0
h-index: 0
机构:
North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R ChinaNorth China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
Ji, Zhengsen
Sun, Lijie
论文数: 0引用数: 0
h-index: 0
机构:
North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R ChinaNorth China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China