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Effect of nano-silica on properties of blended cement 3) Byung-Wan Jo, Chang-Hyun Kim, Ghi-ho Tae, Jong-Bin Park, “ Characteristics of cement mortar with nano-SiO 2 particles,” Construction and Building materials, 21, 1351-1355, 2007. 4) Lu P, Young JF, “Hot pressed DSP cement paste”, Material Research Society Symposium Proceedings, 245,1992. 5) Richard P, Cheyrezy M. reactive powder concretes with high ductility and 200-800 MPa compressive strength, San Francisco: ACI Spring convention, SP 144-124, 1994. 6) Gengying Li,”Properties of high-volume fly ash concrete incorporating nan-SiO 2” , Cement Concrete Research 34, 1043-1049,2004. www.<strong>ijcer</strong>online.com ||May ||2013|| Page 55
International Journal of Computational Engineering Research||Vol, 03||Issue, 5|| Load Forecasting For Practical Power Systems by Using Artificial Neural Networks and Classical Techniques – A Case Study Mr. N.M.G. Kumar 1 , Dr.P.S.Sangameswara Raju 2 , Mr.P.Venkatesh 3 1 Research scholar, Department of EEE, S.V.U. College of Engineering.& Associate Professor, Department of EEE., Sree Vidyanikethan Engineering College . 2 Professor, Department of E.E.E., S.V.U. College of Engineering, Tirupati, Andhra Pradesh, India. 3Asst.Prof, Dept of EEE, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India. ABSTRACT: This paper presents a method of STLF (Short Term Load Forecasting) in electrical power system using ANN. In this a multilayered feed forward network with back propagation learning algorithm is used because of its god generalizing property. The input to the Neural network is in terms of past load data which are heuristically chosen such that they reflect the trend, load shape as well as some influence of weather, but here the weather data is not used to train the network. The network is trained to predict one hour ahead load forecasting in both ANN as well as Conventional (Regression) methods. This method is trained for one month load forecasting data and two months load forecasting data and found that the MAPE(Mean absolute percentage error) obtained is small especially for prediction of load for hourly in considering two months data than one month data to train the network. Keywords: STLF, ANN, Load Forecasting, Load demand I. INTRODUCTION Forecasting of load is what will happen in the future by simulating the loads for certain conditions or trends with the help of available past data. Over the years forecasting has been refined considerably and has now reached a stage where it is more precise and unbiased. In Electrical Power Systems, there is a great need for accurately forecasting the load and energy requirements because electricity generation and distribution are a great financial liability to the State Corporation. Accurate Forecasting is also necessary because availability of electricity is one of the several important factors for agricultural and industrial development. If the Energy Forecast is too conservative, then there will be a chance of generating capacity falling short of the actual demand, resulting in the restrictions being imposed on the power supply, which will affect the economic aspects of well being of the corporation of the state. If the Forecast is too optimistic, it may lead to excess generating capacity resulting in part of the investment not giving immediate returns. A developing country like India the financial resources are minimum, it cannot afford to face the two situations mentioned above. So it must be appropriate to consider Electrical Load Forecasting importance. In order to do proper estimation of load and analyzing its pattern, to receive adequate rate of return of the investment for maximum utilization of the plant, it is important to consider both i) magnitude of the system load and ii) location of these loads. Several electric power companies have adapted the conventional methods for forecasting the future load. In conventional methods, the models are designed based on the relationship between load power and factors influencing load power. The conventional method has the advantage that we can forecast load power with a simple prediction model. However, since the relationship between load power and factors influencing load power is nonlinear, it is difficult to identify its non-linearity by using conventional methods. It is observed that perfect prediction is hardly ever been possible. The thought of using the Artificial Neural Networks gives reasonably good predictions while estimation of loads and model can be utilized effectively when compared with the statistical techniques. It is mainly due to the ability of ANN in supporting a non-linear mapping between input and output variables along with its special features such as robust performance in the noisy and incomplete data environments, high parallel computation and self-learning. General software for Forecasting the load for different scenarios is developed and variables are trained using the MATLAB. STLF is an essential tool in operation and planning of the power system. It helps in coordinating the generation and area interchange to meet the load demand. It also helps in security assessment, dynamic state estimation, load management and other related functions. STLF are primarily www.<strong>ijcer</strong>online.com ||May ||2013|| Page 56
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