Electricity Load Forecasting Using Data Mining Technique

Accurate load forecasting is become crucial in power system operation and planning; both for deregulated and regulated electricity market.A variety of methods including neural networks, time series, hybrid method and fuzzy logic have been developed for load forecasting. The time series techniques have been widely used because load behavior can be analyzed in a time series signal with hourly, daily, weekly, and seasonal periodicities. However, for a huge power system covering large geographical area such as Peninsular Malaysia, a single forecasting model for the entire Malaysia would not satisfy the forecasting accuracy; due to the load and weather diversity. Thus, this research will cater these conditions whereby five models of SARIMA (Seasonal ARIMA) Time Series were developed for five day types

A Review of Considered Factors to Penetrate Renewable Energy Resources in Electrical Power System

As an increasing of load demand, scarcity of fossil fuel and penetration of greenhouse gasses (GHG) effect, utilization of renewable energy resources (RER) such as wind, solar, biomass and tidal are rising drastically. Distributed generation (DG) is a technology giving opportunity to integrate RER into power system network. These integrations are needed optimal long term planning. Those planning, hopefully, can increase reliability of electrical power system network while saving environment from GHG with minimum infestation, operation and maintenance cost. The aim of this paper is reviewing factors should be consider when preparing, operating and evaluating electrical power system with integration of RER. By this planning, it is expected that its integration is effective and efficient in a lifetime of project. Finally, this review can help government, researcher, engineer and private sector to make policies to preparing hybrid power system-DGs.   Keywords: Penetration of renewable energy resources, electrical power system, long term planning, distributed generation, policies &nbsp

ELECTRICITY FORECASTING FOR SMALL SCALE POWER SYSTEM USING ARTIFICIAL NEURAL NETWORK

Short-term load forecast is an essential part of electric power system planning and operation. For this project, the main focus will be on the Gas District Cooling Plant (GDC) which acts as the primary source of energy for Universiti Teknologi PETRONAS (UTP). This project is looking into weekly forecast of the electricity production for the GDC plant using Artificial Neural Network Approach. This forecasting method will be very useful to support plant operation as the trending of load demand for an educational centre such as UTP is very much dependent on the university activities itself. The project involve MATLAB program for the STLF with Artificial Neural Network prediction model. The obtained results showed that introducing Multilayer Perceptron (MLP) Neural Network architecture improve the prediction significantly by obtaining a very small value of Mean Absolute Percent Error (MAPE). Besides that, by getting the smaller value of MAPE, it represents higher forecast accuracy of the model itself. The report consists of an introduction, problem statement, objectives, literature review and methodology used to solve the problem. It further looks into the obtained results with consistent discussion

Household Electricity Load Forecasting Toward Demand Response Program Using Data Mining Techniques in A Traditional Power Grid

At present, the continuous increase of household electricity demand is strategic and crucial in electricity demand management. Household electricity consumers can play an important role in this issue. The rationalization of electricity consumption might be achieved by using an efficient Demand Response (DR) program. In this paper a new methodology is suggested using a combination of data mining techniques namely K-means clustering, K-Nearest Neighbors (K-NN) classification and ARIMA for electricity load forecasting using consumers’ electricity prepaid bills data set of an ordinary electricity grid with prepaid electricity meters. As a result of applying this methodology, various DR programs are recommended as an attempt to assist the management of electricity system to manage the electricity demand issues from demand-side in an efficient and effective manner, which can be put into practice. A case study has been carried out in Tulkarm District, Palestine. The performance of applying the suggested methodology is measured, and the results are considered very well.Keywords: Demand Response (DR); K-means Clustering; K-Nearest Neighbor classification (K-NN); ARIMA model; Prepaid electricity metersJEL Classifications: Q4, Q41, Q47, Q49DOI: https://doi.org/10.32479/ijeep.11192</p

ELECTRICITY FORECASTING FOR SMALL SCALE POWER SYSTEM USING ARTIFICIAL NEURAL NETWORK

Short-term load forecast is an essential part of electric power system planning and operation. For this project, the main focus will be on the Gas District Cooling Plant (GDC) which acts as the primary source of energy for Universiti Teknologi PETRONAS (UTP). This project is looking into weekly forecast of the electricity production for the GDC plant using Artificial Neural Network Approach. This forecasting method will be very useful to support plant operation as the trending of load demand for an educational centre such as UTP is very much dependent on the university activities itself. The project involve MATLAB program for the STLF with Artificial Neural Network prediction model. The obtained results showed that introducing Multilayer Perceptron (MLP) Neural Network architecture improve the prediction significantly by obtaining a very small value of Mean Absolute Percent Error (MAPE). Besides that, by getting the smaller value of MAPE, it represents higher forecast accuracy of the model itself. The report consists of an introduction, problem statement, objectives, literature review and methodology used to solve the problem. It further looks into the obtained results with consistent discussion

NEURAL NETWORK APPLICATION TO SHORT TERM LOAD FORECAST

Power system planning and operation is an important part for power systems industry. By having a good planning and operation, the quality of power supplied will be improved ensuring both consumer and power provider getting their share equally. In this case, the most challenging part is the prediction of how much the load that will be used by the consumer for a short period of time. This prediction is called load forecasting. This will be very useful to every power system company as the trending for the load demand is different for each geographical location. There are different methods to do the load forecasting. One of the project involved MATLAB program for the short term load forecasting (STLF) using Artificial Neural Network (ANN) model. We are using Multilayer Perceptron (MLP) Neural Network architecture, it will improve the forecast value significantly by obtain a very small mean absolute percentage error (MAPE). By getting a smaller MAPE, it represents higher forecast accuracy of the model itself. The elements in this report contain of an introduction, problem statement, objectives, literature review and methodology which was used to solve the forecasting problems. The discussion of the obtained results will be looked further in this project

Cascading Outages Detection and Mitigation Tool to Prevent Major Blackouts

Due to a rise of deregulated electric market and deterioration of aged power system infrastructure, it become more difficult to deal with the grid operating contingencies. Several major blackouts in the last two decades has brought utilities to focus on development of Wide Area Monitoring, Protection and Control (WAMPAC) systems. Availability of common measurement time reference as the fundamental requirement of WAMPAC system is attained by introducing the Phasor Measurement Units, or PMUs that are taking synchronized measurements using the GPS clock signal. The PMUs can calculate time-synchronized phasor values of voltage and currents, frequency and rate of change of frequency. Such measurements, alternatively called synchrophasors, can be utilized in several applications including disturbance and islanding detection, and control schemes. In this dissertation, an integrated synchrophasor-based scheme is proposed to detect, mitigate and prevent cascading outages and severe blackouts. This integrated scheme consists of several modules. First, a fault detector based on electromechanical wave oscillations at buses equipped with PMUs is proposed. Second, a system-wide vulnerability index analysis module based on voltage and current synchrophasor measurements is proposed. Third, an islanding prediction module which utilizes an offline islanding database and an online pattern recognition neural network is proposed. Finally, as the last resort to interrupt series of cascade outages, a controlled islanding module is developed which uses spectral clustering algorithm along with power system state variable and generator coherency information

Cascading Outages Detection and Mitigation Tool to Prevent Major Blackouts

Due to a rise of deregulated electric market and deterioration of aged power system infrastructure, it become more difficult to deal with the grid operating contingencies. Several major blackouts in the last two decades has brought utilities to focus on development of Wide Area Monitoring, Protection and Control (WAMPAC) systems. Availability of common measurement time reference as the fundamental requirement of WAMPAC system is attained by introducing the Phasor Measurement Units, or PMUs that are taking synchronized measurements using the GPS clock signal. The PMUs can calculate time-synchronized phasor values of voltage and currents, frequency and rate of change of frequency. Such measurements, alternatively called synchrophasors, can be utilized in several applications including disturbance and islanding detection, and control schemes. In this dissertation, an integrated synchrophasor-based scheme is proposed to detect, mitigate and prevent cascading outages and severe blackouts. This integrated scheme consists of several modules. First, a fault detector based on electromechanical wave oscillations at buses equipped with PMUs is proposed. Second, a system-wide vulnerability index analysis module based on voltage and current synchrophasor measurements is proposed. Third, an islanding prediction module which utilizes an offline islanding database and an online pattern recognition neural network is proposed. Finally, as the last resort to interrupt series of cascade outages, a controlled islanding module is developed which uses spectral clustering algorithm along with power system state variable and generator coherency information

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications