The electrical grid has become more complex; utilities are revisiting their approaches, methods, and technology to accurately predict energy demands across all time horizons in a timely manner. With the advanced analytics of SAS^® Energy Forecasting, Old Dominion Electric Cooperative (ODEC) provides data-driven load predictions from next hour to next year and beyond. Accurate intraday forecasts means meeting daily peak demands saving millions of dollars at critical seasons and events. Mid-term forecasts provide a baseline to the cooperative and its members to accurately anticipate regional growth and customer needs, in addition to signaling power marketers where, when, and how much to hedge future energy purchases to meet weather-driven demands. Long-term forecasts create defensible numbers for large capital expenditures such as generation and transmission projects. Much of the data for determining load comes from disparate systems such as supervisory control and data acquisition (SCADA) and internal billing systems combined with external market data (PJM Energy Market), weather, and economic data. This data needs to be analyzed, validated, and shaped to fully leverage predictive methods. Business insights and planning metrics are achieved when flexible data integration capabilities are combined with advanced analytics and visualization. These increased computing demands at ODEC are being achieved by leveraging Amazon Web Services (AWS) for expanded business discovery and operational capacity. Flexible and scalable data and discovery environments allow ODEC analysts to efficiently develop and test models that are I/O intensive. SAS^® visualization for the analyst is a graphic compute environment for information-sharing that is memory intensive. Also, ODEC IT operations require deployment options tuned for process optimization to meet service level agreements that can be quickly evaluated, tested, and promoted into production. What was once very difficult for most ut ilities to embrace is now achievable with new approaches, methods, and technology like never before.

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Electric load forecasting is a complex problem that is linked with social activity considerations and variations in weather and climate. Furthermore, electric load is one of only a few goods that require the demand and supply to be balanced at almost real time (that is, there is almost no inventory). As a result, the utility industry could be much more sensitive to forecast error than many other industries. This electric load forecasting problem is even more challenging for holidays, which have limited historical data. Because of the limited holiday data, the forecast error for holidays is higher, on average, than it is for regular days. Identifying and using days in the history that are similar to holidays to help model the demand during holidays is not new for holiday demand forecasting in many industries. However, the electric demand in the utility industry is strongly affected by the interaction of weather conditions and social activities, making the problem even more dynamic. This paper describes an investigation into the various technologies that are used to identify days that are similar to holidays and using those days for holiday demand forecasting in the utility industry.