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.

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 Role of Biofuels in Energy Transition, Green Economy and Carbon Neutrality

2021 ◽  
Vol 13 (22) ◽  
pp. 12374
Author(s):  
Nida Khan ◽  
Kumarasamy Sudhakar ◽  
Rizalman Mamat
Keyword(s):  
Energy Demand ◽  
Fossil Fuels ◽  
First Generation ◽  
Energy Transition ◽  
Sustainable Transportation ◽  
Green Economy ◽  
Carbon Neutrality ◽  
Transportation Sector ◽  
The Social

Modern civilization is heavily reliant on petroleum-based fuels to meet the energy demand of the transportation sector. However, burning fossil fuels in engines emits greenhouse gas emissions that harm the environment. Biofuels are commonly regarded as an alternative for sustainable transportation and economic development. Algal-based fuels, solar fuels, e-fuels, and CO2-to-fuels are marketed as next-generation sources that address the shortcomings of first-generation and second-generation biofuels. This article investigates the benefits, limitations, and trends in different generations of biofuels through a review of the literature. The study also addresses the newer generation of biofuels highlighting the social, economic, and environmental aspects, providing the reader with information on long-term sustainability. The use of nanoparticles in the commercialization of biofuel is also highlighted. Finally, the paper discusses the recent advancements that potentially enable a sustainable energy transition, green economy, and carbon neutrality in the biofuel sector.

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 Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

scholarly journals Edible Energy Production and Energy Return on Investment—Long-Term Analysis of Global Changes

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1011
Author(s):  
Bartłomiej Bajan ◽  
Joanna Łukasiewicz ◽  
Agnieszka Poczta-Wajda ◽  
Walenty Poczta
Keyword(s):  
Energy Efficiency ◽  
Return On Investment ◽  
Food Production ◽  
Energy Production ◽  
Crop Production ◽  
Fossil Fuels ◽  
Animal Production ◽  
Global Changes ◽  
Energy Return On Investment

The projected increase in the world’s population requires an increase in the production of edible energy that would meet the associated increased demand for food. However, food production is strongly dependent on the use of energy, mainly from fossil fuels, the extraction of which requires increasing input due to the depletion of the most easily accessible deposits. According to numerous estimations, the world’s energy production will be dependent on fossil fuels at least to 2050. Therefore, it is vital to increase the energy efficiency of production, including food production. One method to measure energy efficiency is the energy return on investment (EROI), which is the ratio of the amount of energy produced to the amount of energy consumed in the production process. The literature lacks comparable EROI calculations concerning global food production and the existing studies only include crop production. The aim of this study was to calculate the EROI of edible crop and animal production in the long term worldwide and to indicate the relationships resulting from its changes. The research takes into account edible crop and animal production in agriculture and the direct consumption of fossil fuels and electricity. The analysis showed that although the most underdeveloped regions have the highest EROI, the production of edible energy there is usually insufficient to meet the food needs of the population. On the other hand, the lowest EROI was observed in highly developed regions, where production ensures food self-sufficiency. However, the changes that have taken place in Europe since the 1990s indicate an opportunity to simultaneously reduce the direct use of energy in agriculture and increase the production of edible energy, thus improving the EROI.

A Simulation Model for Multi-Factor Sensitivity Analysis on Energy Demand

2012 ◽  
Vol 621 ◽  
pp. 352-355
Author(s):  
Zhong Fu Tan ◽  
Shu Xiang Wang ◽  
Chen Zhang ◽  
Li Qiong Lin ◽  
Yin Hui Zhao
Keyword(s):  
Sensitivity Analysis ◽  
Energy Efficiency ◽  
Regression Model ◽  
Simulation Model ◽  
Influencing Factors ◽  
Energy Demand ◽  
Analysis Model ◽  
Demand Forecast ◽  
Population Urbanization

This paper analyses multi influencing factors of energy demand, using energy demand forecast regression model reveals inner relations between each factor and energy demand. Establish simulation model of the relation between GDP, energy intense and energy demand. Under the change in population, urbanization and energy efficiency, this paper gives analysis model of energy demand change.

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