Affiliations: [a] Orissa Engineering College, Bhubaneswar, India | [b] IIIT, Bhubaneswar, India | [c] Siksha `O' Anusandhan University, Bhubaneswar, India
Correspondence:
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Corresponding author: S. Chakravarty, Orissa Engineering College, Bhubaneswar, India. E-mail:pkdash.india@gmail.com
Abstract: Accurate electricity price forecasting is a key area in the electricity
market. This paper proposes a hybrid model, Evolutionary-Improved Cuckoo
Search Extreme Learning Machine (E-ICSELM) for day and week ahead prediction
of a highly volatile financial time series data i.e. electricity price for
six different energy markets such as Hourly Ontario Electricity Price
(HOEP), Pennsylvania Jersey Maryland (PJM), New England, Nord Pool,
California and Spain. In this model, Improved Cuckoo Search (ICS), a
meta-heuristic, population based optimization techniques used to select
input weights and hidden biases and Moore-Penrose (MP) generalized inverse
to analytically determine the output weights. Experimental results show the
superiority of the proposed E-ICSELM model when it is compared with simple
ELM and Evolutionary-Cuckoo Search based ELM (E-CSELM).
