A Novel Scheme of Load Forecasting pertaining to long term Planning of a distribution system

Chaturvedi, Ashvini; Murthy, M. B. R.; Ranjan, Rakesh; Prasad, K.

doi:10.1109/tencon.2005.301338

Cited by 5 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This enhancement improved the contractor's ability to communicate and coordinate visually among the stakeholders. Additionally, it supports contractors in managing site logistics and monitoring construction progress [57]. Moreover, the significant features of BIM were the digitalizing and visualizing of the project data in 3D, the detection of clashes, and the synchronizing of the changes instantly among project parties.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

BIM Impact on Construction Project Time Using System Dynamics in Saudi Arabia’s Construction

Alnaser,

Alsanabani,

Al-Gahtani

2023

Buildings

View full text Add to dashboard Cite

Construction delays are a prevalent issue worldwide, and Saudi Arabia is just a short distance from this experience. Although the Saudi construction industry was booming due to Vision 2030, major delays were recorded in numerous construction projects, resulting in extra costs and schedule overruns. Studies on Building Information Modeling (BIM) have increased recently due to their crucial role in managing construction projects. However, more efforts must be made to define the impact value of adopting BIM using advanced techniques such as System Dynamics (SD). For this reason, it also aims to demonstrate the BIM impacts globally to encourage taking advantage of such a system. Limited papers studied BIM impact in-depth using advanced techniques such as SD. Thus, A questionnaire was distributed to fifty-nine experts to measure the extent of BIM’s influence on the ten factors affecting project delay times discovered in the literature. The statistical analysis results were used to calculate each factor’s relative importance index (RII), compare them to the results obtained without BIM use, and calculate the relative difference for each factor. These relative differences were considered in the modified SD. The modified SD was applied to a case study representing an academic building in the King Saud University campus. The main finding revealed that BIM decreased construction project delays by 14.55%. In addition, this study found that BIM has a maximum influence on poor site management and supervision by contractors, with a reduction percentage of 17.65%, and a minimum influence on lack of contractor experience and managerial skills, with 11.76%. The SD analysis results confirmed that BIM has a significant impact on reducing construction project delays.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Then, the fitted curve was extended to 100% progress. Three primary components are typical of an S-curve [57]. The early or dormant stage contributes 10%.…”

Section: Compute Bim Influence On Project Delay Factors and Project E...mentioning

confidence: 99%

BIM Impact on Construction Project Time Using System Dynamics in Saudi Arabia’s Construction

Alnaser,

Alsanabani,

Al-Gahtani

2023

Buildings

View full text Add to dashboard Cite

show abstract

“…Traditional methods include spatial [3]- [5], trending [6]- [7], and hybrid, while other unique methods include land use suitability maps [8], data mining [9], segmentation [10], neuro-fuzzy logic [11], econometrics [12], and the use of decision trees [13]. Each method has made an important contribution to the discourse and has also fed the development of Geotypical Growth-based Load Forecasting.…”

Section: Overview Of Load Forecasting Methodsmentioning

confidence: 99%

“…is already common among electrical distribution planning engineers [15]- [17]. However, the amount of data gathering involved with such segmentation does not support a simple method for forecasting long-term megawatt loads.…”

Section: Overview Of Load Forecasting Methodsmentioning

confidence: 99%

Geotypical Growth-based Load Forecasting: An introduction to an innovative approach

Penton

McKinney²

2012

PES T&D 2012

View full text Add to dashboard Cite

An introduction to Geotypical Growth-based Load Forecasting (GGLF), long-term power distribution load forecasting based on biological concepts and segmented geographies, is presented. Using load data obtained from 165 substations in Southern Idaho and Southeastern Oregon, this document (1) describes the reasoning for using Living Systems Theory (LST) as a basis for long-term distribution load forecasting, (2) shows the relationship between the MW growth rates of the substations to their observed peak loads, (3) provides the rationale for segmenting the substations into their various geographical characteristics (geotypes), and (4) discusses a logistical regression curve-fitting model that represents the load characteristics of five example geotypes. Example geotypes discussed in the document are those common to a high plains geography, semi-arid climate type. Recommendations for additional research that applies GGLF to other climate types and to other MW load densities are also suggested.

show abstract

“…Refs. [34,35] develop user classes and apply them to determine the area load expansion capability. The authors of [36] study several methods of forecasting the load of the Greek power system.…”

Section: Large Area Forecastsmentioning

confidence: 99%

Attention-Focused Machine Learning Method to Provide the Stochastic Load Forecasts Needed by Electric Utilities for the Evolving Electrical Distribution System

O’Donnell

2023

Energies

View full text Add to dashboard Cite

Greater variation in electrical load should be expected in the future due to the increasing penetration of electric vehicles, photovoltaics, storage, and other technologies. The adoption of these technologies will vary by area and time, and if not identified early and managed by electric utilities, these new customer needs could result in power quality, reliability, and protection issues. Furthermore, comprehensively studying the uncertainty and variation in the load on circuit elements over periods of several months has the potential to increase the efficient use of traditional resources, non-wires alternatives, and microgrids to better serve customers. To increase the understanding of electrical load, the authors propose a multistep, attention-focused, and efficient machine learning process to provide probabilistic forecasts of distribution transformer load for several months into the future. The method uses the solar irradiance, temperature, dew point, time of day, and other features to achieve up to an 86% coefficient of determination (R2).

show abstract

A Novel Scheme of Load Forecasting pertaining to long term Planning of a distribution system

Cited by 5 publications

References 7 publications

BIM Impact on Construction Project Time Using System Dynamics in Saudi Arabia’s Construction

BIM Impact on Construction Project Time Using System Dynamics in Saudi Arabia’s Construction

Geotypical Growth-based Load Forecasting: An introduction to an innovative approach

Attention-Focused Machine Learning Method to Provide the Stochastic Load Forecasts Needed by Electric Utilities for the Evolving Electrical Distribution System

Contact Info

Product

Resources

About