Information from load profile is useful for electricity suppliers to plan their generation, improving their market strategies and load balancing. Consumers in the new liberalized market have the opportunity of choosing their electricity suppliers between several suppliers and the possibility to access to new products and services from them. Hence they need the knowledge of load profile to help ...

In this paper, we propose a neural network approach to forecast AM/PM Jordan electric power load curves based on several parameters (temperature, date and the status of the day). The proposed method has an advantage of dealing with not only the nonlinear part of load curve but also with rapid temperature change of forecasted day, weekend and special day features. The proposed neural network is ...

This paper describes an application of combined model of extrapolation and correlation techniques for short term load forecasting of an Indian substation. Here effort has been given to improvise the accuracy of electrical load forecasting considering the factors, past data of the load, respective weather condition and financial growth of the people. These factors are derived by curve fitting te...

The electricity planning department of eThekwini Municipality performs a power system load forecast based on individual substation forecasts. The individual substation forecasts have uncertainty. It is desired to understand the effect of these uncertainties on the accuracy of the total system forecast. A Monte Carlo simulation of the forecasted system loadflow was performed. By means of this ap...

Load forecasting has become in recent years one of the major areas of research in electrical engineering. In a deregulated, competitive power market, utilities tend to maintain their generation reserve close to the minimum required by an independent system operator. This creates a need for an accurate instantaneous-load forecast for the next several minutes. An accurate forecast eases the probl...

To respond to volatility and congestion in the power grid, demand response (DR) mechanisms allow for shaping the load compared to a base load profile. When tapping on a large population of heterogeneous appliances as a DR resource, the challenge is in modeling the dimensions available for control. Such models need to strike the right balance between accuracy of the model and tractability. The g...

14 15 Wind power integration has always been a key research area due to the green future power system target. However, the 16 intermittent nature of wind power may impose some technical and economic challenges to Independent System Operators 17 (ISOs) and increase the need for additional flexibility. Motivated by this need, this paper focuses on the potential of Demand 18 Response Programs (DRP...

For large customers, Electricité de France the French national electric power company stores every 10’ the amount of electric power they consume. For each customer, these measures lead to curves called electric load curves. Clustering of electric load curves is a key problem for understanding the behavior of these customers. Several methods have been used but Kohonen maps give a very nice solut...

Developing energy consumption models for smart buildings is important for studying demand response, home energy management, and distribution network simulation. In this work, we develop parsimonious Markovian models of smart buildings for different periods in a day for predicting electricity consumption. To develop these models, we collect two data sets with widely different load profiles over ...

A daily peak load forecasting technique that uses artificial neural network presented in this paper. A neural network of used to predict the daily peak load for a period available using one step ahead prediction load to the actual load. The ith index is used as load for the ith day of the year following networks are trained by the back propagation algorithm. from the Nigerian national electric ...