scholarly journals Pakistan’s Energy Demand Forecasting for Various Sector through Long Range Alternative Planning System

2021 ◽  
Vol 12 (1) ◽  
pp. 20
Author(s):  
Muhammad Mahboob ◽  
Muzaffar Ali ◽  
Tanzeel ur Rashid ◽  
Rabia Hassan
Keyword(s):  
Long Range ◽  
Policy Development ◽  
Energy Demand ◽  
Demand Forecasting ◽  
Planning System ◽  
Reference Scenario ◽  
Demand Forecast ◽  
Energy Forecasting ◽  
Energy Assets

Energy forecasting and policy development needs a detailed evaluation of energy assets and long-term demand estimation. The demand forecast of electricity is an essential portion of energy management, particularly in the formation of electricity. It is necessary to predict electricity needs to avoid the energy deficits or a destabilization between energy demand and supply. In this article, long-range energy alternative planning (LEAP) is used for the modeling of energy and various sectors in Pakistan as a case study. The simulated model comprises three different scenarios, a strong economy, a weak economy, and a medium economy as a reference scenario. The base year is 2015 and the outlook year is 2040. Electricity demands are almost more than four times those of the outlook year, increasing from 7.71 million tons of oil equivalent (MTOE) in 2015 to 29.77 MTOE by the end of 2040.

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.

scholarly journals Long-Term Demand Forecasting in a Scenario of Energy Transition

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3095 ◽  
Author(s):  
Rafael Sánchez-Durán ◽  
Joaquín Luque ◽  
Julio Barbancho
Keyword(s):  
Energy Efficiency ◽  
Energy Demand ◽  
Fossil Fuels ◽  
Demand Forecasting ◽  
Energy Transition ◽  
Energy Concentration ◽  
Demand Forecast ◽  
Term Prediction ◽  
Long Term Prediction

The energy transition from fossil fuels to carbon-free sources will be a big challenge in the coming decades. In this context, the long-term prediction of energy demand plays a key role in planning energy infrastructures and in adopting economic and energy policies. In this article, we aimed to forecast energy demand for Spain, mainly employing econometrics techniques. From information obtained from institutional databases, energy demand was decomposed into many factors and economy-related activity sectors, obtaining a set of disaggregated sequences of time-dependent values. Using time-series techniques, a long-term prediction was then obtained for each component. Finally, every element was aggregated to obtain the final long-term energy demand forecast. For the year 2030, an energy demand equivalent to 82 million tons of oil was forecast. Due to improvements in energy efficiency in the post-crisis period, a decoupling of economy and energy demand was obtained, with a 30% decrease in energy intensity for the period 2005–2030. World future scenarios show a significant increase in energy demand due to human development of less developed economies. For Spain, our research concluded that energy demand will remain stable in the next decade, despite the foreseen 2% annual growth of the nation’s economy. Despite the enormous energy concentration and density of fossil fuels, it will not be affordable to use them to supply energy demand in the future. The consolidation of renewable energies and increasing energy efficiency is the only way to satisfy the planet’s energy needs.

A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting

Energy ◽  
2020 ◽  
Vol 204 ◽  
pp. 117948 ◽  
Author(s):  
Mohammad-Rasool Kazemzadeh ◽  
Ali Amjadian ◽  
Turaj Amraee
Keyword(s):  
Data Mining ◽  
Energy Demand ◽  
Demand Forecasting ◽  
Peak Load ◽  
Energy Demand Forecasting ◽  
Hybrid Data

scholarly journals PEMODELAN ENERGI BARU TERBARUKAN (EBT) MELALUI PENDEKATAN DINAMIS UNTUK KETAHANAN ENERGI KABUPATEN SUMBAWA 2017-2027

2020 ◽  
Vol 4 (2A) ◽  
pp. 122-132
Author(s):  
Nova Aryanto ◽  
Ahmad Jaya ◽  
Chairul Hudaya
Keyword(s):  
Energy Demand ◽  
Central Government ◽  
Systems Approach ◽  
Clean Energy ◽  
Green Energy ◽  
Planning System ◽  
Demand Forecast ◽  
Energy Potential ◽  
General Plan ◽  
The Government

In an effort to increase the value of the Electrification Ratio value reaches 99.9% andUtilization of New and Renewable Energy (EBT) of up to 25% by 2025 is requiredThe General Plan for National Energy (RUEN) which is revealed to be the General DraftRegional Energy (RUED). Sumbawa as an area in West Nusa Tenggarahas the potential for EBT in the form of Solar Energy Potential, Hydro Energy, and Thermal EnergyEarth and Sea Energy require strategic policies to manage andmeet the energy security of the region. This study aims to predictEnergy needs, and mapping the potential of EBT, in order to obtain a mixenergy (energy mix) is balanced. This research was conducted using toolssoftware Long-range Energy Alternatives Planning System (LEAP) withdynamic systems approach. Data obtained from PT. PLN UP3 Sumbawa, RUPTL DataPLN NTB Region, Bapedda Kab. Sumbawa and Data from BPS Kab. Sumbawa. ResultThis research shows that the potential of EBT can be integrated in RUED formeet the energy needs of the region. Therefore, this research canproduce accurate energy demand forecast for Sumbawa Regencyin particular the use of regional green energy sources (Green Energy) to achieve thisenergy security for the great and dignified Sumbawa Regencyencouraging the formation of RUED Sumbawa Regency in line with the Indicator StrategySDGs program launched by the Government, both the Central Government andLocal Government especially the Clean Energy (Green Energy) program.

scholarly journals Electricity, Heat, and Gas Load Forecasting Based on Deep Multitask Learning in Industrial-Park Integrated Energy System

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1355 ◽  
Author(s):  
Linjuan Zhang ◽  
Jiaqi Shi ◽  
Lili Wang ◽  
Changqing Xu
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Energy System ◽  
Multitask Learning ◽  
Effective Characteristics ◽  
Industrial Park ◽  
Energy Forecasting ◽  
Training Stage ◽  
Integrated Energy System ◽  
Term Energy

Different energy systems are closely connected with each other in industrial-park integrated energy system (IES). The energy demand forecasting has important impact on IES dispatching and planning. This paper proposes an approach of short-term energy forecasting for electricity, heat, and gas by employing deep multitask learning whose structure is constructed by deep belief network (DBN) and multitask regression layer. The DBN can extract abstract and effective characteristics in an unsupervised fashion, and the multitask regression layer above the DBN is used for supervised prediction. Then, subject to condition of practical demand and model integrity, the whole energy forecasting model is introduced, including preprocessing, normalization, input properties, training stage, and evaluating indicator. Finally, the validity of the algorithm and the accuracy of the energy forecasts for an industrial-park IES system are verified through the simulations using actual operating data from load system. The positive results turn out that the deep multitask learning has great prospects for load forecast.

scholarly journals Road Transportation Energy Demand and Environmental Emission: A Case of Kathmandu Valley

2016 ◽  
Vol 18 ◽  
pp. 30-40 ◽  
Author(s):  
Iswor Bajracharya ◽  
Nawraj Bhattarai
Keyword(s):  
Energy Demand ◽  
Market Price ◽  
Planning System ◽  
Kathmandu Valley ◽  
Reference Scenario ◽  
Road Transportation ◽  
Cumulative Energy ◽  
Cumulative Energy Demand ◽  
Alternative Scenarios ◽  
Light Duty Vehicles

The current trade embargo imposed by India has created an acute fuel crisis in Nepal which has stranded more than 50% of public vehicles affecting the supply of all the necessities and daily life of people. This study has shown some alternative ways to manage the vehicle fuel demand especially for urban transportation in the Kathmandu valley, Nepal. The modeling tool, Long-range Energy Alternative Planning System (LEAPS) has been used to develop a bottom-up model to estimate the energy demand and environmental emissions in the Kathmandu valley for the period 2016-2030 AD. Besides the Reference scenario, four alternative scenarios (Public Bus Penetration, Improved Fuel Economy, Electric Motorbike and Hybrid Electric Car) have been developed. In the Reference scenario, the cumulative energy demand will reach 142,092 thousand GJ within the analysis period. About 65% of this demand comes from motorbikes and light duty vehicles. If all of the alternative scenarios are implemented together, about 38% of energy demand and 54% of CO2 emission can be avoided compared to the reference scenario within the study period. About 1641 million US$ at the current market price can be avoided within the analysis period if all of these four options are applied together.HYDRO Nepal: Journal of Water Energy and EnvironmentVolume 18, 2016, JanuaryPage 30-40

scholarly journals Energy Demand Forecasting Using Deep Learning: Applications for the French Grid

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2242 ◽  
Author(s):  
Alejandro J. del Real ◽  
Fernando Dorado ◽  
Jaime Durán
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Energy Demand ◽  
Demand Forecasting ◽  
Demand Forecast ◽  
Learning Techniques ◽  
Energy Demand Forecasting ◽  
Artificial Neural

This paper investigates the use of deep learning techniques in order to perform energy demand forecasting. To this end, the authors propose a mixed architecture consisting of a convolutional neural network (CNN) coupled with an artificial neural network (ANN), with the main objective of taking advantage of the virtues of both structures: the regression capabilities of the artificial neural network and the feature extraction capacities of the convolutional neural network. The proposed structure was trained and then used in a real setting to provide a French energy demand forecast using Action de Recherche Petite Echelle Grande Echelle (ARPEGE) forecasting weather data. The results show that this approach outperforms the reference Réseau de Transport d’Electricité (RTE, French transmission system operator) subscription-based service. Additionally, the proposed solution obtains the highest performance score when compared with other alternatives, including Autoregressive Integrated Moving Average (ARIMA) and traditional ANN models. This opens up the possibility of achieving high-accuracy forecasting using widely accessible deep learning techniques through open-source machine learning platforms.

scholarly journals Application of LEAP model on long-term electricity demand forecasting in Indonesia, period 2010-2025

2018 ◽  
Vol 49 ◽  
pp. 02007 ◽  
Author(s):  
Jaka Windarta ◽  
Bambang Purwanggono ◽  
Fuad Hidayanto
Keyword(s):  
Demand Forecasting ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
Planning System ◽  
Large Country ◽  
Electricity Demand Forecasting ◽  
Electricity Planning ◽  
Business As Usual

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.

Electricity Demand Forecasting

2014 ◽  
Vol 2 (1) ◽  
pp. 1-19
Author(s):  
Fawwaz Elkarmi ◽  
Nazih Abu Shikhah
Keyword(s):  
Power System ◽  
Planning Process ◽  
Demand Forecasting ◽  
Energy Forecasting ◽  
Forecasting Models ◽  
Forecasting Methods ◽  
Long Term Forecasting ◽  
Time Frames ◽  
And Control

Forecasting is the backbone of any planning process in all fields of interest. It has a great impact on future decisions. It is also of great importance to the operation and control of business, which is reflected as profits or losses to the entity. This paper aims to provide the planner with sufficient knowledge and background of the different scopes of forecasting methods, in general, and when applied to power system field, in particular. Various load and energy forecasting models, and theoretical techniques are discussed from different perspectives, time frames, and levels. The paper presents the attributes and importance of forecasting through several cases of research conducted by the author for the Jordanian power system. In all cases the methodologies selected cover short, medium and long term forecasting periods and the results are accurate.

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