In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term Load Forecasting (STLF) helps in determining the economic, reliable and secure operating strategies for po...

Short-Term Load Forecasting (STLF) plays an important role for the economic and secure operation of power systems. In this paper, Continuous Genetic Algorithm (CGA) is employed to evolve the optimum large neural networks structure and connecting weights for one-day ahead electric load forecasting problem. This study describes the process of developing three layer feed-forward large neural netwo...

The short-term load forecasting is an important method for security dispatching and economical operation in electric power system, and its prediction accuracy directly affects the operating reliability of the electric system. So the global optimization ability of particle swarm optimization (PSO) algorithm and classification prediction ability of support vector machine (SVM) are combined in ord...

Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet tra...

Accurate models for electric power load forecasting are essential to the operation and planning of a power utility company. Load forecasting helps electric utility to make important decisions on trading of power, load switching, and infrastructure development. Load forecasts are extremely important for power utilizes ISOs, financial institutions, and other stakeholder of power sector. Short ter...

In this paper, the application of neural networks to study the design of short-term load forecasting (STLF) Systems for Illam state located in west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STLF systems was used. Our study based on MLP was trained and tested using three years (2004-2006) data. The results show that MLP networ...

This paper presents the application of Fuzzy ARTMAP neural network for evaluating on-line load forecasting in short term case. A new approach using artificial neural networks (ANNs) is proposed for short term load forecasting. To forecast loads of a day, the hourly load pattern and the maximum and minimum and average of temprature must be determined. To demonstrate the effectiveness of the prop...

This paper presents a novel neural network with a variable structure, which is trained by a real-coded genetic algorithm (RCGA), and its application on short-term load forecasting. The proposed variable-structure neural network (VSNN) consists of a Neural Network with Link Switches (NNLS) and a Network Switch Controller (NSC). In the NNLS, switches are introduced in the links between the hidden...

A clustering based technique has been developed and implemented for Short Term Load Forecasting, in this article. Formulation has been done using Mean Absolute Percentage Error (MAPE) as an objective function. Data Matrix and cluster size are optimization variables. Model designed, uses two temperature variables. This is compared with six input Radial Basis Function Neural Network (RBFNN) and F...