Probabilistic Residential Load Forecasting Based on Micrometeorological Data and Customer Consumption Pattern

2021 ◽  
pp. 1-1
Author(s):  
Lilin Cheng ◽  
Haixiang Zang ◽  
Yan Xu ◽  
Zhinong Wei ◽  
Guoqiang Sun
Keyword(s):  
Load Forecasting ◽  
Consumption Pattern

scholarly journals Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 54992-55008
Author(s):  
Dabeeruddin Syed ◽  
Haitham Abu-Rub ◽  
Ali Ghrayeb ◽  
Shady S. Refaat ◽  
Mahdi Houchati ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Smart Grid ◽  
Load Forecasting ◽  
Consumption Pattern ◽  
Short Term ◽  
Short Term Load Forecasting

ARTIFICIAL NEURAL NETWORK FOR LONG TERM LOAD FORECASTING IN UTTARAKHAND STATE: A CASE STUDY

2021 ◽  
pp. 55-56
Author(s):  
Rakesh Kumar ◽  
Rakesh Ranjan ◽  
Mukesh Verma
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
New Technologies ◽  
Load Forecasting ◽  
Consumption Pattern ◽  
Hydro Power ◽  
Charging Infrastructure ◽  
The Government ◽  
New Infrastructure

Electricity is one of the essential part of our life. With the increase in consumption of resources the demand of electricity is also increased. Uttarakhand as hilly state is approaching towards implementation of new Technologies and Techniques in the area of growth and suistainable development. Due to the implementation of better road infrastructure, tourism connectivity and IoT devices in various projects and inclusion of electric vehicles and their charging infrastructure in Uttarakhand State the demand of electricity has also increased. The Uttarakhand State has planned the establishment of new infrastructure by providing relaxation on various taxes and option of subsidy to investors. The exemption on xed electricity charges is provided to investors in Uttarakhand. The highest part of Electricity Generation is based on Hydro Power in Uttarakhand. By establishment of new infrastructure in Uttarakhand it would be a thrust to load generation companies to produce demanded of electricity on time. In this study the long-term load forecasting from 2022 to 2030 is analysed using Articial Neural Network. The input data is received from Uttarakhand Electricity Regulatory Commission and Uttarakhand Power Corporation Limited. The prediction is based on last 10 years data of historical load, GDP, Population, and past two years data of electric vehicles, and charging infrastructure. In this study, it has reported that by 2030 there would be huge change in infrastructure and most of diesel and petrol vehicles would come on electric vehicles. This study is focused on the Long-Term Load Forecasting in Uttarakhand State where electric vehicles and charging infrastructure load requirement is also calculated. Using Deep Learning Technique in this paper Articial Neural Network is used for forecasting the results. This tool is used to identify the consumption pattern of electricity in Uttarakhand State for further nine years from 2022 to 2030. The Government of Uttarakhand has planned Vision 2030 for the sustainable development in Uttarakhand.

scholarly journals A Short-Term Residential Load Forecasting Model Based on LSTM Recurrent Neural Network Considering Weather Features

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2737
Author(s):  
Yizhen Wang ◽  
Ningqing Zhang ◽  
Xiong Chen
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Large Scale ◽  
Load Forecasting ◽  
Consumption Pattern ◽  
Forecasting Model ◽  
Short Term ◽  
Model Based ◽  
Power Load ◽  
Power Load Forecasting

With economic growth, the demand for power systems is increasingly large. Short-term load forecasting (STLF) becomes an indispensable factor to enhance the application of a smart grid (SG). Other than forecasting aggregated residential loads in a large scale, it is still an urgent problem to improve the accuracy of power load forecasting for individual energy users due to high volatility and uncertainty. However, as an important variable that affects the power consumption pattern, the influence of weather factors on residential load prediction is rarely studied. In this paper, we review the related research of power load forecasting and introduce a short-term residential load forecasting model based on a long short-term memory (LSTM) recurrent neural network with weather features as an input.

Confidence Interval Estimating Method for Load Forecasting with Consideration of Error Causes

2016 ◽  
Vol 136 (6) ◽  
pp. 775-783
Author(s):  
Naoto Ishibashi ◽  
Akihiro Kabasawa ◽  
Tatsuya Iizaka ◽  
Tohru Katsuno
Keyword(s):  
Confidence Interval ◽  
Load Forecasting

Consumption based-only load forecasting for individual households in nanogrids: a case study

2020 ◽  
Author(s):  
Martina Caliano ◽  
Amedeo Buonanno ◽  
Giorgio Graditi ◽  
Antonino Pontecorvo ◽  
Gianluca Sforza ◽  
...  
Keyword(s):  
Load Forecasting

Factor Related With the Anemia Incidence Of Adolescent Girl in Private High School Santa Lusia Medan, 2019

2020 ◽  
Vol 4 (2) ◽  
pp. 33
Author(s):  
Risda Mariana Manik ◽  
Hetty Gustina Simamora
Keyword(s):  
High School ◽  
Adolescent Girl ◽  
Sampling Technique ◽  
Consumption Patterns ◽  
Reproductive Age ◽  
Digital Data ◽  
Deficiency Anemia ◽  
Consumption Pattern ◽  
Cross Sectional ◽  
Private High School

According to data Basic Healt Research (Riskesdas) in 2016, as many as 22,7% women of reproductive age more than 15 years indicate anemia. Anemia that often occurs is iron deficiency anemia, the incidence reaches 50% of the total anemia. There is a significant relationship between nutritional status and incidence of anemia in adolescent.This study was an observational analytic with a cross sectional approach. This study was conducted in private high school Santa Lusia Medan. The population of this study were adolescent girl with a total sample 74 using total sampling technique. The research material in the form scales and height measurements to measure boddy mass index, quesioner for iron consumption patterns and consumption habits of Fe tablet and hemoglobin levels were examined using haemometer digital. Data analysis used chi square test (α=0,005).The results of the research are variabels related to anemia incidence are body mass index (sig=0,019), iron consumption patterns (sig=0,017), Fe tablet consumption habit (sig=0,045). Conclusion this study is factor causing anemia in adolescent girls is the pattern of iron consumption. Adolescent girl who have irreguler iron consumption pattern have an oppurtunity to experience anemia of 4,250 compared to adolescent girl who have reguler iron consumption patterns.

Support vector approach using data augmentation with copula function applied to load forecasting

2019 ◽  
Author(s):  
Gabriel Ribeiro ◽  
Marcos Yamasaki ◽  
Helon Vicente Hultmann Ayala ◽  
Leandro Coelho ◽  
Viviana Mariani
Keyword(s):  
Data Augmentation ◽  
Load Forecasting ◽  
Copula Function ◽  
Support Vector ◽  
Using Data ◽  
Vector Approach

An Analysis of Consumption Pattern in Pakistan

1982 ◽  
Vol 21 (4) ◽  
pp. 275-296 ◽  
Author(s):  
Rehana Siddiqui
Keyword(s):  
Growth Rates ◽  
Rural Areas ◽  
Household Size ◽  
Consumption Pattern ◽  
Total Expenditure ◽  
Demand Growth ◽  
Food Items ◽  
Urban Rural ◽  
Urban And Rural Areas

The paper aims at testing the validity of Engel's law with data on Pakistan. Consumption functions for urban and rural areas have been estimated separately. These functions are shown to be determined by total expenditure and household size. Engel's law is confirmed for some commodity groups but not for all. Following tests of urban-rural homogeneity and of stability of urban and rural consumption functions, demand growth rates for different food and non-food items have been calculated, assuming different growth rates of total expenditure and household size.

Energy Consumption Pattern of Residential Buildings: Case Study of Residential Area in Batu Pahat, Johor

2020 ◽  
Vol 12 (9) ◽  
Author(s):  
Junaidah Jailani ◽  
◽  
Norsyalifa Mohamad ◽  
Muhammad Amirul Omar ◽  
Hauashdh Ali ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Residential Buildings ◽  
Residential Area ◽  
Consumption Pattern ◽  
Air Conditioners ◽  
Residential Sector ◽  
Waste Energy ◽  
Main Factor ◽  
Energy Consumption Pattern

According to the National Energy Balance report released by the Energy Commission of Malaysia in 2016, the residential sector uses 21.6% of the total energy in Malaysia. Residents waste energy through inefficient energy consumption and a lack of awareness. Building occupants are considered the main factor that influences energy consumption in buildings, and to change energy consumption on an overall scale, it is crucial to change individual behaviour. Therefore, this study focused on analysing the energy consumption pattern and the behaviour of consumers towards energy consumption in their homes in the residential area of Batu Pahat, Johor. A self-administrated questionnaire approach was employed in this study. The findings of this study showed that the excessive use of air conditioners was a significant factor in the increasing electricity bills of homeowners as well as the inefficient use of electrical appliances. Also, this study determined the effect of awareness on consumer behaviour. This study recommends ways to help minimise energy consumption in the residential area.

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