This paper focuses on the study of short term load forecasting (STELF) using interval Type-2 Fuzzy Logic (IT2FL) and feed-forward Neural Network with back-propagation (NN-BP) tuning algorithm to improve their approximation capability, flexibility and adaptiveness. IT2FL for STELF is presented which provides additional degrees of freedom for handling more uncertainties for improving prediction a...

Power distribution system in the country is characterized by high power losses, poor reliability of supply, frequent equipment burnouts etc. The traditional approach in electric power generation is to have centralized plants distributing electricity through an extensive transmission & distribution network. Distribution generation (dg) provides electric power at a site closer to the customer, el...

With the ever-increasing complexities in power systems across the worldwide, especially opening of electric power markets, it becomes more and more important to provide stable, secure, controlled and high quality electric power on today’s environment. The deregulation and competitive environment in the contemporary power networks will imply a new scenario in terms of load and power flow conditi...

The modern biomechanical two-phase poroelastic model of bone tissue is presented. Bone tissue is treated in this model as a porous elastically deformed solid filled with a viscous newtonian fluid. Traditional one-phase biomechanical model of bone tissue, which is characterized by the Young modulus and the Poisson's coefficient, is still valid and it can be treated as an approximate model in com...

This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (Volt/Var Control: VVC) in electric power systems considering voltage security. VVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an on-line VVC strategy with continuous and discrete control va...

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 ...

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...

In response to the governmental policy of saving energy sources and reducing CO2, and carry out the resident quality of local; this paper proposes a new method for a non-intrusive load-monitoring (NILM) system in smart home to implement the load identification of electric equipments and establish the electric demand management. Non-intrusive load-monitoring techniques were often based on power ...

In order to efficiently improve the prediction accuracy, two load forecasting model based on fuzzy theory are presented, which are fuzzy clustering model and improved fuzzy regression analysis model .The method of fuzzy clustering is used to divide the area by the similar feature of load increasing. The new division is promising to improve the result of evident degree of clustering index to pow...

Accurate daily peak load forecasts are important for secure and profitable operation of modern power utilities, with deregulation and competition demanding ever-increasing accuracies. Machine learning techniques including neural and abductive networks have been used for this purpose. Network committees have been proposed for improving regression and classification accuracy in many disciplines, ...