R. Bhatnagar

Bio: R. Bhatnagar is an academic researcher from Virginia Tech. The author has contributed to research in topics: Control system & Economic dispatch. The author has an hindex of 3, co-authored 3 publications receiving 498 citations.

An expert system based algorithm for short term load forecast

Abstract: Existing studies on 1-24 hr load forecasting algorithms are reviewed, and an expert-system-based algorithm is presented as an alternative. The logical and syntactical relationships between weather and load as well as the prevailing daily load shapes have been examined to develop the rules for this approach. Two separate, but similar, algorithms have been developed to provide 1-6 hr and 24 hr forecasts. These forecasts have been compared with observed hourly load data for a Virginia electric utility for all seasons of the year. The 1 hr and 6 hr forecast errors (absolute average) ranged from 0.869% to 1.218% and from 2.437% to 3.48% respectively. The 24 hour forecast errors (absolute average) ranged from 2.429% to 3.300%. >

Dispatch of Direct Load Control for Fuel Cost Minimization

Abstract: The concept, algorithm and the requisite mathematics for the dispatch of Direct Load Control (DLC) for fuel cost-minimization is developed. The dispatch decision is based on the consideration of producing fuel cost savings. The dispatch is integrated with the economic dispatch of conventional generation. The algorithm for the dispatch and coordination of DLC takes into account all operational constraints on the dispatch of DLC. A method is thus provided for rendering the DLC program flexible to changing system requirements. DLC can be integrated into system operations and utilized for peak shaving and/or fuel cost savings, as and when required.

An Introduction to Fuzzy Control

Abstract: Fuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic. They can be found either as stand-alone control elements or as int ...

Electric load forecasting using an artificial neural network

Abstract: An artificial neural network (ANN) approach is presented for electric load forecasting. The ANN is used to learn the relationship among past, current and future temperatures and loads. In order to provide the forecasted load, the ANN interpolates among the load and temperature data in a training data set. The average absolute errors of the 1 h and 24 h-ahead forecasts in tests on actual utility data are shown to be 1.40% and 2.06%, respectively. This compares with an average error of 4.22% for 24 h ahead forecasts with a currently used forecasting technique applied to the same data. >

Energy models for demand forecasting—A review

Abstract: Energy is vital for sustainable development of any nation – be it social, economic or environment. In the past decade energy consumption has increased exponentially globally. Energy management is crucial for the future economic prosperity and environmental security. Energy is linked to industrial production, agricultural output, health, access to water, population, education, quality of life, etc. Energy demand management is required for proper allocation of the available resources. During the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs. In this paper an attempt is made to review the various energy demand forecasting models. Traditional methods such as time series, regression, econometric, ARIMA as well as soft computing techniques such as fuzzy logic, genetic algorithm, and neural networks are being extensively used for demand side management. Support vector regression, ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL and LEAP are also being used at the national and regional level for energy demand management.

Short-term load forecasting

Abstract: This paper discusses the state of the art in short-term load forecasting (STLF), that is, the prediction of the system load over an interval ranging from one hour to one week. The paper reviews the important role of STLF in the on-line scheduling and security functions of an energy management system (EMS). It then discusses the nature of the load and the different factors influencing its behavior. A detailed classification of the types of load modeling and forecasting techniques is presented. Whenever appropriate, the classification is accompanied by recommendations and by references to the literature which support or expand the discussion. The paper also presents a lengthy discussion of practical aspects for the development and usage of STLF models and packages. The annotated bibliography offers a representative selection of the principal publications in the STLF area.