Swasti R. Khuntia

Neural Network-based Load Forecasting and Error Implication for Short-term Horizon

Load forecasting is considered vital along with many other important entities required for assessing the reliability of power system. Thus, the primary concern is not to forecast load with a novel model, rather to forecast load with the highest accuracy. Short-term load forecast accuracy is often hindered due to various load impacting factors. Two of the major impacting factors are day-ahead weather forecast and subsequent variation in electricity demand that is independent of weather. To tackle the uncertainty in short-term load forecasting, this paper presents a neural network-based load forecasting technique for short-term horizon based on data corresponding to a U.S. independent system operator. With the real life data, a better understanding of forecasting error is carried out while further identifying the time periods when the load is supposedly to be over- or under-forecast.

Classification, domains and risk assessment in asset management: A literature study

This paper presents a literature study on asset management in electrical power transmission and distribution system. Due to restructure and deregulation of electric power industry in recent times, the focus has been on transmission and distribution assets that include transmission lines, transformers, power plants, substations and support structures. The study aims to provide a first of its kind exposure to asset management classification, various interesting maintenance methods and theories developed in last two decades. In the end, it also discusses various risk assessment techniques in asset management developed and used for academic research and industries.

Volatility in electrical load forecasting for long-term horizon — An ARIMA-GARCH approach

Electrical load forecasting in long-term horizon of power systems plays an important role for system planning and development. Load forecast in long-term horizon is represented as time-series. Thus, it is important to check the effect of volatility in the forecasted load time-series. In short, volatility in long-term horizon affects four main actions: risk management, long-term actions, reliability, and bets on future volatility. To check the effect of volatility in load series, this paper presents a univariate time series-based load forecasting technique for long-term horizon based on data corresponding to a U.S. independent system operator. The study employs ARIMA technique to forecast electrical load, and also the analyzes the ARCH and GARCH effects on the residual time-series.

Mutual information based Bayesian analysis of power system reliability

This paper aims at assessing the power system reliability by estimating loss of load (LOL) index using mutual information based Bayesian approach. Reliability analysis is a key component in the design, analysis and tuning of complex structure like electrical power system. Consideration is given to rare events while constructing the Bayesian network, which provides reliable estimates of probability distribution function of LOL with lesser computing effort. Also, the ranking of load components due to loss of load is evaluated. The RBTS and IEEE RTS-24 systems are used as test cases.