Multi-year load growth-based optimal planning of grid-connected microgrid considering long-term load demand forecasting: A case study of Tehran, Iran

2020 ◽  
Vol 42 ◽  
pp. 100827
Author(s):  
Jamal Faraji ◽  
Hamed Hashemi-Dezaki ◽  
Abbas Ketabi
Author(s):  
Bing Han ◽  
Mingxuan Li ◽  
Jingjing Song ◽  
Junjie Li ◽  
Jamal Faraji

In this article, an optimal on-grid MicroGrid (MG) is designed considering long-term load demand prediction. Multilayer Perceptron (MLP) Artificial Neural Network (ANN) has been used for time-series load prediction. Yearly demand growth has also been considered in the optimization process based on the forecasted load profile. Two case studies have been performed with the forecasted and historical load profiles, respectively. It has been shown that by applying the forecasted load profile, realistic results of net present cost (NPC), cost of energy (COE) and MG configuration would be achieved. Moreover, it has been demonstrated that utilizing battery storage systems (BSSs) are not economic in the proposed system. The introduced MG also produces lower emission compared to the system with the historical load profile.


2014 ◽  
Vol 505-506 ◽  
pp. 915-921
Author(s):  
Shi Chao Sun ◽  
Zheng Yu Duan ◽  
Chuan Chen

Freight transport demand forecasting as one of the basis in urban logistics planning, is not only an important premise of designing a variety of logistics development policies and infrastructure constructions, but also a key indicator to measure whether the logistics planning is reasonable. This paper addresses the methods of the freight transport demand forecasting in urban logistics planning based on a case study of Yiwu city. Considering the change of long-term trend emphatically, conventional trend extrapolation method, regression analysis method, elasticity coefficient method, linear exponential smoothing method and grey model are applied to predict the logistics demand of Yiwu city respectively. Then the results of five kinds of forecasting methods are analyzed to obtain the final forecasting logistics demand.


2018 ◽  
Vol 49 ◽  
pp. 02007 ◽  
Author(s):  
Jaka Windarta ◽  
Bambang Purwanggono ◽  
Fuad Hidayanto

Electricity demand forecasting is an important part in energy management especially in electricity planning. Indonesia is a large country with a pattern of electricity consumption which continues to increase, therefor need to forecasting electricity demand in order to avoid unbalance demand and supply or deficit energy. LEAP (Long-range Energy Alternative Planning System) as a tool energy model and Indonesia as a case study. Basically, electricity demand is influenced by population, economy and electricity intensity. The purpose of this study is to provide understanding and application of electricity demand forecasting by using LEAP. The base year is 2010 and end year projection is 2025. The scenarios of simulated model consist of two scenarios. They are Business as Usual (BAU) and Government policy scenario. Results of both scenarios indicate that end year electricity demand forecasting in Indonesia increased more than two fold compared to base year.


2020 ◽  
Vol 29 (4) ◽  
pp. 2049-2067
Author(s):  
Karmen L. Porter ◽  
Janna B. Oetting ◽  
Loretta Pecchioni

Purpose This study examined caregiver perceptions of their child's language and literacy disorder as influenced by communications with their speech-language pathologist. Method The participants were 12 caregivers of 10 school-aged children with language and literacy disorders. Employing qualitative methods, a collective case study approach was utilized in which the caregiver(s) of each child represented one case. The data came from semistructured interviews, codes emerged directly from the caregivers' responses during the interviews, and multiple coding passes using ATLAS.ti software were made until themes were evident. These themes were then further validated by conducting clinical file reviews and follow-up interviews with the caregivers. Results Caregivers' comments focused on the types of information received or not received, as well as the clarity of the information. This included information regarding their child's diagnosis, the long-term consequences of their child's disorder, and the connection between language and reading. Although caregivers were adept at describing their child's difficulties and therapy goals/objectives, their comments indicated that they struggled to understand their child's disorder in a way that was meaningful to them and their child. Conclusions The findings showed the value caregivers place on receiving clear and timely diagnostic information, as well as the complexity associated with caregivers' understanding of language and literacy disorders. The findings are discussed in terms of changes that could be made in clinical practice to better support children with language and literacy disorders and their families.


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