Urban long term electricity demand forecast method based on system dynamics of the new economic normal: The case of Tianjin

Energy ◽  
2017 ◽  
Vol 133 ◽  
pp. 9-22 ◽  
Author(s):  
Yongxiu He ◽  
Jie Jiao ◽  
Qian Chen ◽  
Sifan Ge ◽  
Yan Chang ◽  
...  
Keyword(s):  
System Dynamics ◽  
Electricity Demand ◽  
Forecast Method ◽  
Demand Forecast

Long-Term Electricity Demand Forecast Using Multivariate Regression and End-Use Method: A Study Case of Maluku-Papua Electricity System

2021 ◽  
Author(s):  
Tumiran Tumiran ◽  
Sarjiya Sarjiya ◽  
Lesnanto Multa Putranto ◽  
Edwin Nugraha Putra ◽  
Rizki Firmansyah Setya Budi ◽  
...  
Keyword(s):  
Multivariate Regression ◽  
Electricity Demand ◽  
Demand Forecast ◽  
Electricity System ◽  
Study Case ◽  
End Use

scholarly journals Long Term Household Electricity Demand Forecasting Based on RNN-GBRT Model and a Novel Energy Theft Detection Method

2021 ◽  
Vol 11 (18) ◽  
pp. 8612
Author(s):  
Santanu Kumar Dash ◽  
Michele Roccotelli ◽  
Rasmi Ranjan Khansama ◽  
Maria Pia Fanti ◽  
Agostino Marcello Mangini
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Sudden Change ◽  
Electricity Demand ◽  
Gradient Boosting ◽  
Forecasting Model ◽  
Demand Forecast ◽  
Daily Consumption ◽  
Energy Provider

The long-term electricity demand forecast of the consumer utilization is essential for the energy provider to analyze the future demand and for the accurate management of demand response. Forecasting the consumer electricity demand with efficient and accurate strategies will help the energy provider to optimally plan generation points, such as solar and wind, and produce energy accordingly to reduce the rate of depletion. Various demand forecasting models have been developed and implemented in the literature. However, an efficient and accurate forecasting model is required to study the daily consumption of the consumers from their historical data and forecast the necessary energy demand from the consumer’s side. The proposed recurrent neural network gradient boosting regression tree (RNN-GBRT) forecasting technique allows one to reduce the demand for electricity by studying the daily usage pattern of consumers, which would significantly help to cope with the accurate evaluation. The efficiency of the proposed forecasting model is compared with various conventional models. In addition, by the utilization of power consumption data, power theft detection in the distribution line is monitored to avoid financial losses by the utility provider. This paper also deals with the consumer’s energy analysis, useful in tracking the data consistency to detect any kind of abnormal and sudden change in the meter reading, thereby distinguishing the tampering of meters and power theft. Indeed, power theft is an important issue to be addressed particularly in developing and economically lagging countries, such as India. The results obtained by the proposed methodology have been analyzed and discussed to validate their efficacy.

Long-term electricity demand forecast and supply side scenarios for Pakistan (2015–2050): A LEAP model application for policy analysis

Energy ◽  
2018 ◽  
Vol 165 ◽  
pp. 512-526 ◽  
Author(s):  
Nayyar Hussain Mirjat ◽  
Muhammad Aslam Uqaili ◽  
Khanji Harijan ◽  
Gordhan Das Walasai ◽  
Md Alam Hossain Mondal ◽  
...  
Keyword(s):  
Policy Analysis ◽  
Electricity Demand ◽  
Supply Side ◽  
Demand Forecast ◽  
Model Application

scholarly journals Impact of the COVID-19 Pandemic on Electricity Demand and Load Forecasting

2021 ◽  
Vol 13 (3) ◽  
pp. 1435
Author(s):  
Feras Alasali ◽  
Khaled Nusair ◽  
Lina Alhmoud ◽  
Eyad Zarour
Keyword(s):  
Forecast Error ◽  
Moving Average ◽  
Forecast Model ◽  
Electricity Demand ◽  
Demand Forecast ◽  
Forecast Performance ◽  
Artificial Neural Network Ann ◽  
Electrical Demand ◽  
The Impact

The current COVID-19 pandemic and the preventive measures taken to contain the spread of the disease have drastically changed the patterns of our behavior. The pandemic and movement restrictions have significant influences on the behavior of the environment and energy profiles. In 2020, the reliability of the power system became critical under lockdown conditions and the chaining in the electrical consumption behavior. The COVID-19 pandemic will have a long-term effect on the patterns of our behavior. Unlike previous studies that covered only the start of the pandemic period, this paper aimed to examine and analyze electrical demand data over a longer period of time with five years of collected data up until November 2020. In this paper, the demand analysis based on the time series decomposition process is developed through the elimination of the impact of times series correlation, trends, and seasonality on the analysis. This aims to present and only show the pandemic’s impacts on the grid demand. The long-term analysis indicates stress on the grid (half-hourly and daily peaks, baseline demand and demand forecast error) and the effect of the COVID-19 pandemic on the power grid is not a simple reduction in electricity demand. In order to minimize the impact of the pandemic on the performance of the forecasting model, a rolling stochastic Auto Regressive Integrated Moving Average with Exogenous (ARIMAX) model is developed in this paper. The proposed forecast model aims to improve the forecast performance by capturing the non-smooth demand nature through creating a number of future demand scenarios based on a probabilistic model. The proposed forecast model outperformed the benchmark forecast model ARIMAX and Artificial Neural Network (ANN) and reduced the forecast error by up to 23.7%.

scholarly journals Transforming a Liability into an Asset: A System Dynamics Model for Free-Ranging Dog Population Management

Systems ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 56
Author(s):  
Urmila Basu Mallick ◽  
Marja H. Bakermans ◽  
Khalid Saeed
Keyword(s):  
System Dynamics ◽  
Social Integration ◽  
Unintended Consequences ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Innovative Strategies ◽  
Management Context ◽  
Free Ranging ◽  
Social Rewards

Using Indian free-ranging dogs (FRD) as a case study, we propose a novel intervention of social integration alongside previously proposed methods for dealing with FRD populations. Our study subsumes population dynamics, funding avenues, and innovative strategies to maintain FRD welfare and provide societal benefits. We develop a comprehensive system dynamics model, featuring identifiable parameters customizable for any management context and imperative for successfully planning a widescale FRD population intervention. We examine policy resistance and simulate conventional interventions alongside the proposed social integration effort to compare monetary and social rewards, as well as costs and unintended consequences. For challenging socioeconomic ecological contexts, policy resistance is best overcome by shifting priority strategically between social integration and conventional techniques. The results suggest that social integration can financially support a long-term FRD intervention, while transforming a “pest” population into a resource for animal-assisted health interventions, law enforcement, and conservation efforts.

Using system dynamics to evaluate renewable electricity development in Malaysia

Kybernetes ◽  
2014 ◽  
Vol 43 (1) ◽  
pp. 24-39 ◽  
Author(s):  
Salman Ahmad ◽  
Razman bin Mat Tahar
Keyword(s):  
System Dynamics ◽  
Fossil Fuels ◽  
Systems Approach ◽  
Capacity Expansion ◽  
Assessment Model ◽  
Flow Diagram ◽  
Content Type ◽  
The Government ◽  
Policy Targets

Purpose – The purpose of this paper is to provide an assessment of Malaysia's renewable capacity target. Malaysia relies heavily on fossil fuels for electricity generation. To diversify the fuel-mix, a technology-specific target has been set by the government in 2010. Considering the complexity in generation expansion, there is a dire need for an assessment model that can evaluate policy in a feedback fashion. The study also aims to expand policy evaluation literature in electricity domain by taking a dynamic systems approach. Design/methodology/approach – System dynamics modelling and simulation approach is used in this study. The model variables, selected from literature, are constituted into casual loop diagram. Later, a stock and flow diagram is developed by integrating planning, construction, operation, and decision making sub-models. The dynamic interactions between the sub-sectors are analysed based on the short-, medium- and long-term policy targets. Findings – Annual capacity constructions fail to achieve short-, medium- and long-term targets. However, the difference in operational capacity and medium- and long-term target are small. In terms of technology, solar photovoltaic (PV) attains the highest level of capacity followed by biomass. Research limitations/implications – While financial calculations are crucial for capacity expansion decisions, currently they are not being modelled; this study primarily focuses on system delays and exogenous components only. Practical implications – A useful model that offers regulators and investors insights on system characteristics and policy targets simultaneously. Originality/value – This paper provides a model for evaluating policy for renewable capacity expansion development in a dynamic context, for Malaysia.

scholarly journals Decision making in reverse logistics using system dynamics

2004 ◽  
Vol 14 (2) ◽  
pp. 259-272 ◽  
Author(s):  
P. Georgiadis ◽  
D. Vlachos
Keyword(s):  
Decision Making ◽  
System Dynamics ◽  
Reverse Logistics ◽  
Closed Loop ◽  
System Modeling ◽  
New Approach ◽  
Helpful Tool ◽  
Starting Point ◽  
Decision Making Tool

Reverse logistics is a modern field of consideration, research and study, providing helpful information on the operation of the closed-loop supply chain. Although the starting point of this field is traced back to the early 90?s, no standard method has been suggested, neither prevailed. The purpose of this paper is to introduce a new approach on the study of reverse logistics. It is actually a review on how System Dynamics (SD) can be a helpful tool when it is used in the reverse logistics field. The paper explains the basic theory of the system modeling and next it utilizes the reverse logistics model. Finally, an illustrative example shows how SD modeling can be used to produce a powerful long-term decision-making tool.

scholarly journals Testing successful Business Model using System Dynamics

2019 ◽  
Vol 7 ◽  
pp. 91
Author(s):  
J. Ganzarain ◽  
M. Ruiz ◽  
J.I. Igartua
Keyword(s):  
System Dynamics ◽  
Business Model ◽  
Business Models ◽  
Industrial Sector ◽  
Continuous Change ◽  
Dynamic Complexity ◽  
Holistic View ◽  
Short And Long Term ◽  
Systemic View

In our increasingly globalised economy, managing continuous change whilst remaining competitive and dynamic has become a central issue for firms in the industrial sector. One of the elements for obtaining this competitiveness is the value creation model of the firm. The most important challenges in firms are characterised by dynamic complexity which makes it difficult to understand factors in their context. Consequently management and decision making is hindered (Antunes et al., 2011). Business models are characterised by complexity and dynamism. Performance of the firm is a complex topic determined by the large amount of variables that can be involved in the system, and the different effects that influence the system in the short and long term. Due to this complexity a systemic view is required, that is, an holistic view of the whole system. Such a systemic view enables managers to make decisions based on evidence rather than intuition and personal experiences, as they understand how the whole system works. Thus, the main aim of this research is to use an empirical tool such as System Dynamics (SD), to support and sustain firms in the identification of new constructs related to their Business Model (BM).

Sign in / Sign up

Export Citation Format

Share Document