scholarly journals A Stochastic Approach to Energy Policy and Management: A Case Study of the Pakistan Energy Crisis

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2424 ◽  
Author(s):  
Zaman Sajid ◽  
Asma Javaid
Keyword(s):  
Energy Consumption ◽  
Energy Policy ◽  
Stochastic Analysis ◽  
Probabilistic Approach ◽  
Energy Utilization ◽  
Energy Crisis ◽  
Energy Data ◽  
Consumption Data ◽  
The Government ◽  
Energy Theft

The energy policy of a country dictates its ability to better manage and deal with an energy crisis. A sustainable energy policy deals with not only energy production but also with energy consumption. In the past, the government of Pakistan has lacked such an approach. This study aims to develop a policy-making framework to improve the energy management of Pakistan through a probabilistic approach. Stochastic analysis is performed in this study and the uncertainty in energy data is used to propose a holistic energy policy. Energy-utilization data from 17 different sources are used to compare the accuracy of energy-consumption data from 1989 to 2013. The analysis reveals that there exists an uncertainty in energy-consumption data and the major cause of this uncertainty is energy theft. The analysis shows that the industry has the highest uncertainty in its energy-data utilization, followed by the transport and the domestic sectors of Pakistan. Based on stochastic analysis, seven recommended energy-policy guidelines are presented to manage the energy crisis in the country. The analysis proposes that Pakistan needs to take measures to control energy theft.

Novelty Detection in Human Behavior through Analysis of Energy Utilization

2013 ◽  
pp. 65-85 ◽  
Author(s):  
Chao Chen ◽  
Diane J. Cook
Keyword(s):  
Energy Consumption ◽  
Human Behavior ◽  
Novelty Detection ◽  
Electricity Consumption ◽  
Energy Utilization ◽  
Smart Environments ◽  
Data Sets ◽  
Smart Environment ◽  
Energy Data ◽  
Using Data

The value of smart environments in understanding and monitoring human behavior has become increasingly obvious in the past few years. Using data collected from sensors in these environments, scientists have been able to recognize activities that residents perform and use the information to provide context-aware services and information. However, less attention has been paid to monitoring and analyzing energy usage in smart homes, despite the fact that electricity consumption in homes has grown dramatically. In this chapter, the authors demonstrate how energy consumption relates to human activity through verifying that energy consumption can be predicted based on the activity that is being performed. The authors then automatically identify novelties in human behavior by recognizing outliers in energy consumption generated by the residents in a smart environment. To validate these approaches, they use real energy data collected in their CASAS smart apartment testbed and analyze the results for two different data sets collected in this smart home.

scholarly journals Model Fuzzy Tsukamoto untuk Klasifikasi dalam Prediksi Krisis Energi di Indonesia

2016 ◽  
Vol 3 (3) ◽  
pp. 185
Author(s):  
Achmad Zaki ◽  
Heru Agus Santoso
Keyword(s):  
Energy Consumption ◽  
Natural Gas ◽  
Energy Demand ◽  
Fossil Fuels ◽  
Fuzzy Inference ◽  
Energy Crisis ◽  
World Energy ◽  
The Government ◽  
Inference Systems ◽  
Safe Condition

Krisis energi dunia juga terjadi di Indonesia. Cadangan energi di Indonesia terutama energi fosil (minyak bumi, batubara, dan gas alam) semakin hari semakin menyusut. Ketersediaan akan energi fosil juga semakin berkurang karena peningkatan konsumsi energi per kapita. Untuk memprediksi krisis energi di Indonesia, paper ini mengusulkan pengembangan sistem inferensi fuzzy sukamoto untuk klasifikasi krisis energi berdasarkan parameter jumlah produksi, konsumsi energi dan faktor penggerak kebutuhan energi, yakni GDP dan populasi penduduk. Luaran dari sistem ini adalah klasifikasi berdasarkan parameter tersebut, yaitu kondisi aman, waspada dan krisis. Hasil eksperimen menunjukan sistem yang dibangun menghasilkan tingkat akurasi pada minyak bumi 90%, batubara 100 % dan gas alam 100%. Dengan adanya sistem ini diharapkan mampu memberikan peringatan dini dan pendukung keputusan bagi pemerintah atau pihak instansi terkait dalam memberikan penangan atau solusi terhadap masalah krisis energi. World energy crisis also occurred in Indonesia. Energy reserves in Indonesia, especially fossil fuels (petroleum, coal, and natural gas) are increasingly shrinking. The availability of fossil energy will also be on the wane because of an increase in energy consumption per capita. To predict the energy crisis in Indonesia, this paper proposes the development of sukamoto fuzzy inference systems for classification energy crisis based on parameters the amount of production, energy consumption and energy demand driven factors, namely GDP and population. Outcomes of this system is the classification based on these parameters, i.e., a safe condition, alert and crisis. The experimental results show the system produce levels of accuracy at 90% petroleum, natural gas 100% and CoA, 100%. This system are expected to provide an early warning and decision support for the government or the relevant agencies in giving the handlers or the solution to the problem of energy crisis. 

scholarly journals Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 873 ◽  
Author(s):  
Amin Ullah ◽  
Kilichbek Haydarov ◽  
Ijaz Ul Haq ◽  
Khan Muhammad ◽  
Seungmin Rho ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Clustering Algorithm ◽  
Energy Utilization ◽  
Self Organizing Map ◽  
Residential Areas ◽  
Energy Management Systems ◽  
Consumption Data ◽  
Som Clustering ◽  
Low Dimensional ◽  
High Level

The exponential growth in population and their overall reliance on the usage of electrical and electronic devices have increased the demand for energy production. It needs precise energy management systems that can forecast the usage of the consumers for future policymaking. Embedded smart sensors attached to electricity meters and home appliances enable power suppliers to effectively analyze the energy usage to generate and distribute electricity into residential areas based on their level of energy consumption. Therefore, this paper proposes a clustering-based analysis of energy consumption to categorize the consumers’ electricity usage into different levels. First, a deep autoencoder that transfers the low-dimensional energy consumption data to high-level representations was trained. Second, the high-level representations were fed into an adaptive self-organizing map (SOM) clustering algorithm. Afterward, the levels of electricity energy consumption were established by conducting the statistical analysis on the obtained clustered data. Finally, the results were visualized in graphs and calendar views, and the predicted levels of energy consumption were plotted over the city map, providing a compact overview to the providers for energy utilization analysis.

Cultivating Energy, Reducing Poverty: Biofuel Development in an Indonesian Village

2008 ◽  
Vol 7 (2) ◽  
pp. 113-132 ◽  
Author(s):  
Sonny Yuliar ◽  
Ida Nurlaila ◽  
Sulfikar Amir
Keyword(s):  
Energy Policy ◽  
Energy Crisis ◽  
West Java ◽  
High Unemployment ◽  
Biofuel Policy ◽  
Policy Impacts ◽  
The Government

AbstractThis article examines biofuel development in Indonesia initiated by the government through a presidential instruction issued in January 2006. Three layers of analysis are presented. One is related to the politics of oil during the Suharto era that shaped Indonesian energy policy for decades. The second layer looks at current political and economic situations in Indonesia, which has been much affected by a crisis of energy resulting from previous regimes. A biofuel initiative thus got underway to solve three major problems, namely, the energy crisis, high unemployment, and poverty. Finally, this paper observes how the biofuel policy impacts people in a village in West Java.

Semantic Meta Model for the Description of Resource and Energy Data in the Energy Data Management Cycle

2017 ◽  
Vol 871 ◽  
pp. 69-76 ◽  
Author(s):  
Markus Brandmeier ◽  
Matthias Brossog ◽  
Jörg Franke
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Data Management ◽  
Management Systems ◽  
Energy Data ◽  
Meta Model ◽  
Efficiency Measures ◽  
Consumption Data ◽  
Definition Of ◽  
Management Cycle

Energy efficiency is a critical competitive factor. Transparency of energy consumption is the key for increasing efficiency of production. For this purpose, existing energy data management systems collect data such as power, gas or water consumption on field level, save them in databases, and aggregate them in reports. However, the identification of saving potentials and the definition of efficiency measures is carried out by energy experts and thus is dependent on a person’s knowledge. The documentation of knowledge about saving potentials and measures does not take place and relations among data and knowledge of various domains are not captured. In this paper, we provide an approach that allows the holistic capture and description of data and knowledge relations. Through the use of an ontology-based meta model, consumption data can be augmented with information about time and place of capture, data type, intended purpose and permissions, as well as interfaces to other systems and relations to knowledge elements. The semantic model is to capture relevant requirements of all information demanders within the energy data management cycle. Therefore, the model is capable of detecting efficiency deficits and retrieving relevant energy efficiency measures within a knowledge base. Thus, energy consumption data can be efficiently used and knowledge about efficiency can be sustainably preserved.

NREL's Clean Energy Policy Analyses Project. 2009 U.S. State Clean Energy Data Book

2009 ◽  
Author(s):  
Racel Gelman ◽  
Marissa Hummon ◽  
Joyce McLaren ◽  
Elizabeth Doris
Keyword(s):  
Energy Policy ◽  
Clean Energy ◽  
Energy Data ◽  
Data Book ◽  
Clean Energy Policy

scholarly journals An Improved Forecasting Method and Application of China’s Energy Consumption under the Carbon Peak Target

2021 ◽  
Vol 13 (15) ◽  
pp. 8670
Author(s):  
Xiwen Cui ◽  
Shaojun E ◽  
Dongxiao Niu ◽  
Dongyu Wang ◽  
Mingyu Li
Keyword(s):  
Sustainable Development ◽  
Energy Consumption ◽  
Environmental Pollution ◽  
Optimization Methods ◽  
Support Vector ◽  
The Sustainable Development ◽  
Consumption Data ◽  
Forecasting Method ◽  
Forecasting Performance ◽  
Carbon Peak

In the process of economic development, the consumption of energy leads to environmental pollution. Environmental pollution affects the sustainable development of the world, and therefore energy consumption needs to be controlled. To help China formulate sustainable development policies, this paper proposes an energy consumption forecasting model based on an improved whale algorithm optimizing a linear support vector regression machine. The model combines multiple optimization methods to overcome the shortcomings of traditional models. This effectively improves the forecasting performance. The results of the projection of China’s future energy consumption data show that current policies are unable to achieve the carbon peak target. This result requires China to develop relevant policies, especially measures related to energy consumption factors, as soon as possible to ensure that China can achieve its peak carbon targets.

scholarly journals How to Effectively Control Energy Consumption Growth in China’s 29 Provinces: A Paradigm of Multi-Regional Analysis Based on EAALMDI Method

2021 ◽  
Vol 13 (3) ◽  
pp. 1093
Author(s):  
Yunlong Zhao ◽  
Geng Kong ◽  
Chin Hao Chong ◽  
Linwei Ma ◽  
Zheng Li ◽  
...  
Keyword(s):  
Energy Consumption ◽  
High Resolution ◽  
Energy Policy ◽  
Policy Making ◽  
Regional Analysis ◽  
Policy Implications ◽  
Consumption Growth ◽  
High Resolution Analysis ◽  
Resolution Analysis ◽  
Consumption Control

Controlling energy consumption to reduce greenhouse gas emissions has become a global consensus in response to the challenge of climate change. Most studies have focused on energy consumption control in a single region; however, high-resolution analysis of energy consumption and personalized energy policy-making, for multiple regions with differentiated development, have become a complicated challenge. Using the logarithmic mean Divisia index I (LMDI) decomposition method based on energy allocation analysis (EAA), this paper aims to establish a standard paradigm for a high-resolution analysis of multi-regional energy consumption and provide suggestions for energy policy-making, taking 29 provinces of China as the sample. The process involved three steps: (1) determination of regional priorities of energy consumption control by EAA, (2) revealing regional disparity among the driving forces of energy consumption growth by LMDI, and (3) deriving policy implications by comparing the obtained results with existing policies. The results indicated that 29 provinces can be divided into four groups, with different priorities of energy consumption control according to the patterns of coal flows. Most provinces have increasing levels of energy consumption, driven by increasing per capita GDP and improving living standards, while its growth is restrained by decreasing end-use energy intensity, improving energy supply efficiency, and optimization of industrial structures. However, some provinces are not following these trends to the same degree. This indicates that policy-makers must pay more attention to the different driving mechanisms of energy consumption growth among provinces.

scholarly journals Energetic and exergetic analysis of a convective drier: A case study of potato drying process

2020 ◽  
Vol 5 (1) ◽  
pp. 563-572
Author(s):  
Iman Golpour ◽  
Mohammad Kaveh ◽  
Reza Amiri Chayjan ◽  
Raquel P. F. Guiné
Keyword(s):  
Energy Consumption ◽  
Research Work ◽  
Specific Energy Consumption ◽  
Exergy Efficiency ◽  
Operating Conditions ◽  
Energy Utilization ◽  
Convective Drying ◽  
Drying Time ◽  
Energy And Exergy ◽  
Exergy Losses

AbstractThis research work focused on the evaluation of energy and exergy in the convective drying of potato slices. Experiments were conducted at four air temperatures (40, 50, 60 and 70°C) and three air velocities (0.5, 1.0 and 1.5 m/s) in a convective dryer, with circulating heated air. Freshly harvested potatoes with initial moisture content (MC) of 79.9% wet basis were used in this research. The influence of temperature and air velocity was investigated in terms of energy and exergy (energy utilization [EU], energy utilization ratio [EUR], exergy losses and exergy efficiency). The calculations for energy and exergy were based on the first and second laws of thermodynamics. Results indicated that EU, EUR and exergy losses decreased along drying time, while exergy efficiency increased. The specific energy consumption (SEC) varied from 1.94 × 105 to 3.14 × 105 kJ/kg. The exergy loss varied in the range of 0.006 to 0.036 kJ/s and the maximum exergy efficiency obtained was 85.85% at 70°C and 0.5 m/s, while minimum exergy efficiency was 57.07% at 40°C and 1.5 m/s. Moreover, the values of exergetic improvement potential (IP) rate changed between 0.0016 and 0.0046 kJ/s and the highest value occurred for drying at 70°C and 1.5 m/s, whereas the lowest value was for 70°C and 0.5 m/s. As a result, this knowledge will allow the optimization of convective dryers, when operating for the drying of this food product or others, as well as choosing the most appropriate operating conditions that cause the reduction of energy consumption, irreversibilities and losses in the industrial convective drying processes.

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