Electric load forecasting is essential for developing a power supply strategy to improve the reliability of the ac power line data network and provide optimal load scheduling for developing countries where the demand is increased with high growth rate. In this paper, a short-term load forecasting realized by a generalized neuron–wavelet method is proposed. The proposed method consists of wavele...

In order to improve forecasting accuracy of cooling load, this paper applies support vector machine (SVM) model with modified parallel cat swarm optimization (MPCSO) to forecast next-day cooling load in district cooling system(DCS). By extracting the Eigen value of the input historical load data, principal component analysis (PCA) algorithm is used to reduce the complexity of the data sequence....

This paper presents a new approach using Combined Artificial Neural Network (CANN) module for daily peak load forecasting. Five different computational techniques –Constrained method, Unconstrained method, Evolutionary Programming (EP), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) – have been used to identify the CANN module for peak load forecasting. In this paper, a set of ne...

Accurate electric load forecasting has become the most important issue in energy management; however, electric load demonstrates a seasonal/cyclic tendency from economic activities or the cyclic nature of climate. The applications of the support vector regression (SVR) model to deal with seasonal/cyclic electric load forecasting have not been widely explored. The purpose of this paper is to pre...

This paper picturesquely depicts the comparison of different methodologies adopted for predicting the load demand and highlights the changing trend and values under new circumstances using latest non analytical soft computing techniques employed in the field of electrical load forecasting. A very clear advocacy about the changing trends from conventional and obsolete to the modern techniques is...

In restructuring the electric power industry, the load had an important role for market managers and participants when they develop strategies or make decisions to maximize their profit. Therefore, accurate short term load forecasting (STLF) becomes more and more vital for all market participants such as customer or producer in competitive electricity markets. In this paper, a new hybrid algori...

In this paper, we present a framework for implementing short-term load forecasting, in which not only conventional statistical time series prediction methods, but also the AI-based ones, can be configured properly. Besides the prediction methods, the forecasting performance also rely heavily on the quality of training data. Therefore, the proposed framework in this paper has two main characteri...

Short term electrical load forecasting is critical in ensuring reliability and operational efficiency for electrical systems. With an influx of monitoring data and the growing technical complexity of the grid, there is a great interest and need for accurate forecasting in electricity planning. Our project uses a curated electric load dataset from Kaggle and evaluates the performance of several ...

An integrated Genetic Algorithm (GA) based Support Vector Machine (SVM) model for short term load forecasting with input factors selection procession is presented in this paper. First, load distance method is used to for selecting main load influential factors which have more relevant to load parameter. Next, principal component analysis (PCA) technique is used to diminish the correlations amon...

Electric load forecasting is essential to improve the reliability of the ac power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a short-term load forecasting realized by a neural fuzzy network (NFN) and a modified genetic algorithm (GA) is proposed. It can forecast the hourly load accurately with respect to different day types and weather in...