ANN-Based Short-Term Load Forecasting

ANN-Based Short-Term Load Forecasting

DOI: 10.4018/978-1-5225-4941-3.ch006


Load forecasting is a very crucial issue for the operational planning of electrical power systems. In the sixth chapter, it is formulated that a reliable power network along with load prediction models is essential for uninterrupted supply of electrical energy to the consumers. The Back-Propagation ANN algorithm is applied to forecast the load of the power system. Based on the load forecasted power components, transmission lines and sub-stations are augmented for improved reliability in a province.
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Energy is the prime mover of economic growth and development. There is a direct correlation between the degree of economic growth and per capita consumption of energy. The countries with abundant supply of energy have realized substantially higher rates of industrial growth and corresponding increase in the gross national product. As energy sets the basic foundation for the economic development of a country, the energy consumption is bound to grow over the years. A reliable extra high-tension (EHT) power network (Power Grid) along with effective electric load prediction models is essential for uninterrupted supply of electrical power.

Andhra Pradesh power grid has around eight 400 kV, sixty-five 220 kV and one-hundred-and-seventy 132 kV EHT substations. Electrical power reaches the consumer through the network of these substations. Availability and Reliability of these EHT feeders coupled with timely availability of electric power is of paramount importance for ensuring continuous electrical power to the end user which makes electric load forecasting essential for the utilities especially in the context of deregulated environment.

Power system grid is connected to different types of loads like industrial loads, commercial loads, domestic loads, etc. These loads are not constant and they keep on changing hour by hour, day by day, week by week and month by month depending upon various factors like seasonal, geographical and climatic conditions.

To meet the demands of forthcoming day or forthcoming week, proper load scheduling is to be done. For this scheduling of power, to keep the grid stable and provide quality power energy consumption of next day or next week should be known accurately, this process of predetermining the load on the system is called as load forecasting.

Load forecasting helps an electric utility to make important decisions including decisions on purchasing and generating electric power, load switching and infrastructure development. Load forecasts are extremely important for energy suppliers, financial institutions and other participants in electric energy generation, transmission, distribution and markets.

Load forecasting has always been important for planning and operational decisions conducted by utility companies. However, with the deregulation of the energy industries, load forecasting is even more important. With supply and demand fluctuating and the changes of weather conditions and energy prices increasing by a factor of ten or more during peak situations, load forecasting is vitally important for utilities.

Electric load forecasting in power systems is very important task for ensuring reliability and economical operation. There are three different types of load forecasting namely long-term, medium-term and short-term:

  • Short-Term Load Forecasting (STLF): For the day-to-day operation and scheduling of the power system.

  • Medium-Term Load Forecasting (MTLF): Mainly for the scheduling of fuel supplies and maintenance programs. It usually covers a period of few weeks.

  • Long-Term Load Forecasting (LTLF): Mainly the long-term forecasting covers a period of 10 to 20 years. The key factors involved in LTLF are level and type of economic activity, price of electricity, price of substitute sources of energy, non-economic factors such as marketing and conservation campaigns, standby of electricity equipment and weather conditions.

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