scholarly journals Improve the accuracy of short-term forecasting algorithms by Standardized Load Profile and Support Regression Vector: Case study Vietnam

2019 ◽  
Vol 4 (5) ◽  
pp. 243-249
Author(s):  
Nguyen Tuan Dung ◽  
Nguyen Thanh Phuong
Keyword(s):  
Short Term ◽  
Load Profile ◽  
Vector Case ◽  
Short Term Forecasting

scholarly journals Short-Term Electric Load Forecasting Using Standardized Load Profile (SLP) And Support Vector Regression (SVR)

2019 ◽  
Vol 9 (4) ◽  
pp. 4548-4553
Author(s):  
N. T. Dung ◽  
N. T. Phuong
Keyword(s):  
Machine Learning ◽  
Electricity Market ◽  
Business Strategy ◽  
Learning Algorithm ◽  
Load Forecasting ◽  
Support Vector ◽  
Forecast Errors ◽  
Short Term ◽  
Load Profile ◽  
Short Term Forecasting

Short-term load forecasting (STLF) plays an important role in business strategy building, ensuring reliability and safe operation for any electrical system. There are many different methods used for short-term forecasts including regression models, time series, neural networks, expert systems, fuzzy logic, machine learning, and statistical algorithms. The practical requirement is to minimize forecast errors, avoid wastages, prevent shortages, and limit risks in the electricity market. This paper proposes a method of STLF by constructing a standardized load profile (SLP) based on the past electrical load data, utilizing Support Regression Vector (SVR) machine learning algorithm to improve the accuracy of short-term forecasting algorithms.

Case Study: Analysis and Predictions of Shipment Load in Festive Season

2017 ◽  
Vol 7 (7) ◽  
pp. 491
Author(s):  
Megha Chhabra
Keyword(s):  
Time Series Models ◽  
Multiplicative Decomposition ◽  
Short Term ◽  
Case Study Analysis ◽  
Use Of Time ◽  
Load Demand ◽  
Huge Impact ◽  
Short Term Forecasting ◽  
Festive Season

A time-phased forecasting in rest of the year has a huge impact shipping costs, however during a festive season of the year, well predicted and analyzed re-engineering of shipment load plays a major role in bringing up sales. The major concern of the customer is to get delivery on-time, whereas that of the wholesaler / retailer is to provide delivery without any complaint in order to retain the customer. In the framework of competitive supply chain market, necessary accurate Shipping load forecasting tools are required. With the focus of improving prediction accuracy, this case study presents use of Time-series models, multiplicative decomposition model (MDM) and smoothening techniques, on shipping load demand of Arora-Ludhiana-Handlooms during festive seasons for short-term forecasting.

A New Approach in Short-Term Prediction of the Electrical Charge with Regression Models A Case Study

2013 ◽  
Vol 4 (3) ◽  
pp. 34-46
Author(s):  
Farhad Soleimanian Gharehchopoghi ◽  
Freshte Dabaghchi Mokri ◽  
Maryam Molany
Keyword(s):  
Power Systems ◽  
Distribution Systems ◽  
Short Term ◽  
Forecasting Error ◽  
Load Fluctuation ◽  
Distribution Company ◽  
Short Term Forecasting ◽  
West Azerbaijan ◽  
Short Term Prediction

The accuracy of forecasting of electrical load for the electricity industry has a vital significance in the renewal of economic structure as well as various equations including: purchasing and producing energy, load fluctuation, and the development of infrastructures. Its short-term forecasting has a significant role in designing and utilizing power systems and in the distribution systems and having a variety of systems used to maintain security potentials for the system. In this paper, we attempted to carry out a short-term forecasting of electrical distribution company in west Azerbaijan state in Iran's electricity in a few days on the basis of regression multi linear model. This forecasting which was done during a three-day period is and categorized weekdays into three groups including working days, weekends, and holidays was carried out in an hourly manner. This model regardless of parameters like humidity, wind velocity, daylight time, etc. by minimizing the forecasting error managed to maximize the reliability of the results as well as the safety potential of the system. In this model the only influential parameter on the forecasting was the reliance of the forecasting day on previous days. The main purpose of the present study was to maximize the accuracy and reliability of forecasting for certain days (religious holidays, national holidays …). In this paper, the authors managed to decrease the error of forecasting for particular and regular off days to a great extent.

scholarly journals LOAD FORECASTING FOR MONTHS OF THE LUNAR NEW YEAR HOLIDAY USING STANDARDIZED LOAD PROFILE AND SUPPORT REGRESSION VECTOR: CASE STUDY HO CHI MINH CITY

2020 ◽  
Vol 1 (1) ◽  
pp. 01-06
Author(s):  
Tuan- Dung Nguyen ◽  
Thanh- Phuong Nguyen
Keyword(s):  
Machine Learning ◽  
Electricity Market ◽  
Load Forecasting ◽  
Machine Learning Algorithms ◽  
Business Strategies ◽  
Forecast Errors ◽  
Short Term ◽  
Load Profile ◽  
Lunar Calendar ◽  
Vector Case

Load forecasting plays an important role in building business strategies, ensuring reliability and safe operation for any electrical system. There are many different methods, including: regression models, time series, neural networks, expert systems, fuzzy logic, machine learning and statistical algorithms used for short-term forecasts. However, the practical requirement is how to minimize the forecast errors to prevent power shortages or wastage in the electricity market and limit risks. For Asian countries (such as Vietnam) that use lunar calendar, one of the most difficult and unpredictable issues is the Lunar New Year (usually in late January or early February). There is a deviation between the solar calendar and the lunar calendar (the load models are not identical). Therefore, it often leads the forecast results of algorithm for this period with large errors. The paper proposes a method of short-term load forecasting by constructing a Standardized Load Profile (SLP) based on the past electrical load data, combining machine learning algorithms Support Regression Vector (SVR) to improve the accuracy of load forecasting algorithms.

scholarly journals Case Study

2019 ◽  
pp. 85-96
Author(s):  
Maria Jacob ◽  
Cláudia Neves ◽  
Danica Vukadinović Greetham
Keyword(s):  
Chronological Order ◽  
Short Term ◽  
Short Term Forecasting

Abstract In the previous chapter, we looked at load measurements for all households together and we ignored their chronological order. In contrast, in this chapter, we are interested in short term forecasting of household profiles individually. Therefore, information about the time at which measurements were taken becomes relevant.

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