Energy producers, grid operators, and traders must make decisions based on an estimate of future load on the electrical grid. As a result, accurate forecasts of energy load are both a necessity and a business advantage. The vast amounts of data available today have made it possible to create highly accurate forecast models. The challenge lies in developing data analytics workflows that can turn this raw data into actionable insights. A typical workflow involves four steps, each of which brings its own challenges:. Using this application, utility analysts can select any region in the state of New York to see a plot of past energy load and predicted future load Figure 1.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Corpus ID: An integrated demand-planning and sales forecasting model : a case study in Parmalat S.