Journal
APPLIED ENERGY
Volume 258, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2019.114087
Keywords
Electricity price forecasting; VMD; SAPSO; SARIMA; DBN
Categories
Funding
- National Natural Science Foundation of China [71774054]
- Fundamental Research Funds for the Central Universities [2019MS055]
- China Scholarship Council (the International Clean Energy Talent Programme)
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With the large-scale renewable energy integration into the power grid, the features of electricity price has become more complex, which makes the existing models hard to obtain a satisfactory results. Hence, more accurate and stable forecasting models need to be developed. In this paper, a new adaptive hybrid model based on variational mode decomposition (VMD), self-adaptive particle swarm optimization (SAPSO), seasonal auto-regressive integrated moving average (SARIMA) and deep belief network (DBN) is proposed for short term electricity price forecasting. The effectiveness of the proposed model is verified by using data from Australian, Pennsylvania-New Jersey-Maryland (PJM) and Spanish electricity markets. Empirical results show that the proposed model can significantly improve the forecasting accuracy and stability.
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