Abstract
In this paper, we formulate an optimisation-hedging model which demonstrates how operational research methods and analytics can take advantage of big data sources to inform business decisions in the renewable energy sector. This is achieved by incorporating an analytical technique called co-cluster (copula clustering) algorithm in measuring risks confronting a renewable energy producer. The model development and co-cluster methodology are illustrated using an empirical case study under three market scenarios for an ethanol producer. Our results show that adopting the co-cluster algorithm gives the ethanol processor an improved risk management strategy by capturing marginal relationships among the input and output prices; hence highlighting the advantages of big data and data analytics in business decision making within the renewable energy sector.
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