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Energy Demand Forecasting www.sas.com/india
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lectricity is traded in a free market as a commodity, but electricity cannot be stored in warehouses. It must be consumed at the same moment it is produced; otherwise the surplus product is lost. As the Indian energy markets de-regulate; on a 24x7 basis, India needs to maintain the energy equilibrium. Availability Based Tariff (ABT), the tariff structure recommended by Central Electricity Regulatory Commission, sets the course towards de-regulated power market. The main objective of the recommendations is to introduce a tariff regime that will promote responsibility and accountability in power generation and consumption so that overall quality of power in India is improved. It forms the plan for anticipating power consumption and producing the right amount of electricity, every 15 minutes over a 24-hour period. ABT is a scientific methodology for bringing rational tariff structure for supply of electricity from generators to the distribution companies apart from the fact that it is a mechanism for enforcing discipline in the grid. It has a system of rewards and penalties seeking to enforce day ahead pre-committed schedules. The generation tariff under the ABT regime has three components namely the fixed charge; the variable charge; and the unscheduled Inter-change charge (UI Charge). UI charge is payable both by the beneficiary and the generator for the deviations from the schedule, depending upon the prevailing frequency. The beneficiary is liable to pay in case it is under or over draws power from the grid. In case of the generator the liability comes into effect when it generates more
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Sushil Anand
or less than the prescribed schedule. This institutes a commercial mechanism for improving the grid discipline and establishing a frequency regime. There is a paradigm shift from maximum power to maximum reliability. Under the ABT regime, each distribution utility will need to provide their load requirements for ninety six fifteen-minute intervals on a daily basis. The beneficiary is expected to stick to this schedule. Failure to conform to the schedule will attract penalty in the form of UI charges. Thus it becomes critical for the distribution companies to have accurate short-term load forecasting systems in place. Once the ABT regime moves to further de-regulation, long term forecasting will also become crucial, as it will lay the basis on which the distribution companies can enter into long term power purchase agreements with the generating utilities. Any unscheduled deviation from the power generation schedule will incur considerable penalties for the power generation utilities. They will require pro-active management plan through accurate forecasts for ABT optimisation. Even though the UI charges are payable both by the distribution and the generation companies, it does not mean that load forecasting is not applicable to the transmission companies. They require it to accurately predict the demand in different regions in order to provide enough bandwidth for different transmission lines. Deviations in the demand as a result of climatic conditions, festivals, holidays etc. should be considered by the transmission companies in order to ensure smooth transmission and co-
ordination between the beneficiaries and the generators. Transmission companies also require long term energy demand forecasts as part of their expansion. Load forecasting is thus vitally important for the energy industry in the deregulated economy, including India. It has many applications including energy purchasing, generation, load switching, contract valuations, and infrastructure development. The ability to accurately predict the volume of demand will bring significant financial rewards to those who do well. However, many companies are constrained by lack of integration, flexibility and functionality in their current demand forecasting systems. Creating accurate demand forecasts requires high quality of data that is frequently updated. Unfortunately, key data items such as historical metered demand data and weather forecasts are often of poor quality. The statistical software framework should automate the process of data extraction, validation and cleansing, thereby enabling users to concentrate on understanding and exploiting the information contained within the demand forecasts, rather than on their production. Energy demand is dependent upon a host of factors such as time, climatic conditions, special events, census data, appliance sales data, customer segments as well as economic and end use data. Short-term load forecasting process should consider factors such as time, weather data, and customer segments (Household, Industrial, and Agricultural). The time dimension will include variables such as time of the year, the day of the week, and the hour of the
day. Day of the week is further classified into weekends, weekdays, extended weekends and holidays. The load on different weekdays also can behave differently. For instance, Mondays and Fridays being contiguous to weekends, may have structurally different load patterns than Tuesday through Thursday. Customer segment and seasonality also play an important role in determining these patterns. Forecasting holidays is always more difficult than non-holidays since of their intermittent occurrence. In case of major holidays such as Diwali, we should consider the same day last year along with prevailing energy demand in arriving at the forecasts for the current year. Weather parameters are the most important factors in short-term load forecasts. Temperature, humidity rainfall and sunlight hours are among the key weather parameters which should be considered for short-term load forecasts. The medium-and long-term forecasts take into account the historical load, weather data, and census data along with its projections, the number of customers in different categories, appliances in the area, appliance sales data, and demographic profile of the regions among others. Long term forecasts also take into consideration the land-usage patterns and land development plans of the respective regions. Comprehensive forecasting methodology is suggested on multitechnique (statistical, neural network, rule based) approach to model building. A number of forecasting methods have been developed. A variety of methods, such as similar day approach, regression models, time series, neural networks, expert systems, and fuzzy logic are used for short-term forecasting. The statistical software framework should generate forecasts by intelligently determining the forecasting models that best fit the historical data. An appropriate model should be generated for each time series being forecasted based on user-defined criteria. For instance, the pattern for 6.15 AM time-series would be completely different from the one at 6.15 PM timeseries. Model parameters should be intelligently optimized to provide the bestfitting model, resulting in more responsive and accurate forecasts. Business drivers and events (regressors) should be automatically selected from any number
“The demand forecasting solution should strive for self learning capability which automates the process of model tuning avoiding the reliance on user’s intervention therefore improving its adaptability to changing energy consumption behavior and evolution. The exercise should also take into consideration the holdout samples so that forecasting models are selected not only by how well they fit the past data, but by how well they are likely to predict the future.”
of supplied regressors. This approach can be applied to produce segmentation of customers, as well as use it to generate forecasts for any period – whether it is daily, hourly or every 15 minutes. The demand forecasting solution should strive for self learning capability which automates the process of model tuning avoiding the reliance on user’s intervention therefore improving its adaptability to changing energy consumption behavior and evolution. The exercise should also take into consideration the holdout samples so that forecasting models are selected not only by how well they fit the past data, but by how well they are likely to predict the future. The demand forecasting solution should provide for top-down, middle-out and bottom-up forecast reconciliation to support the hierarchical nature of many forecasting processes, ensuring accuracy at the discom level (macro) and the substation level (micro). Energy Demand forecasting models work in a dynamic and volatile environment and therefore need to be continually monitored and improved. The
statistical software framework should provide users with the control they need. No longer will data disappear into a “black box” and mysteriously come out to the other end as a demand forecast. The development, improvements, and analysis of the appropriate statistical tools will lead to the development of more accurate load, forecasting techniques ideally over a comprehensive statistical software framework. Present outlook of the rapidly changing power sector in India, necessitate the development and use of more sophisticated and relevant energy forecasting and statistical tools and methods like never before. Energy demand forecasting is becoming an essential tool for energy management, maintenance scheduling and planning decisions in the liberalized energy markets with fluctuating global fuel prices. \\
Sushil Anand SAS India
egov
february 2010
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