Overview of the Global Electricity System in Oman Considering Energy Demand Model Forecast

Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand.In addition,proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network.As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman,new opportunities may arise considering the efficiency and reliability of the power system;like price-based demand response programs.These programs could either be a large scale for household,commercial or industrial users.However,excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure.Consequently,in this paper,an overview of the Oman regulatory regime,financial mechanism,price control,and distribution system security standard were presented.More so,the energy demand forecast in Oman was analysed,using the econometric model to forecasts its energy peak demand.The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters(by region,and by tariff),considering a case study of Mazoon Electricity Distribution Company(MZEC),which is one of the major power distribution companies in Oman,for effective energy demand in the power grid.

Hybrid LEAP modeling method for long-term energy demand forecasting of regions with limited statistical data

An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited in many regions. In this paper, on the basis of comprehensive literature review, we proposed a hybrid model based on the long-range alternative energy planning (LEAP) model to improve the accuracy of energy demand forecasting in these regions. By taking Hunan province, China as a typical case, the proposed hybrid model was applied to estimating the possible future energy demand and energy-saving potentials in different sectors. The structure of LEAP model was estimated by Sankey energy flow, and Leslie matrix and autoregressive integrated moving average (ARIMA) models were used to predict the population, industrial structure and transportation turnover, respectively. Monte-Carlo method was employed to evaluate the uncertainty of forecasted results. The results showed that the hybrid model combined with scenario analysis provided a relatively accurate forecast for the long-term energy demand in regions with limited statistical data, and the average standard error of probabilistic distribution in 2030 energy demand was as low as 0.15. The prediction results could provide supportive references to identify energy-saving potentials and energy development pathways.

Economic development, energy demand, and carbon emission prospects ofChina's provinces during the 14th Five-Year Plan period: Application ofCMRCGE model

This study focuses on a national regional coordinated development strategy and adopts China Multi-Regional Computable General Equilibrium model to analyze the economic and social development, energy demand, and carbon emissions of the provinces during the 14th Five-Year Plan (FYP, 2021 2025) period based on the economic development and energy demand since the New Normal. The main conclusions are the following: 1) Under the guidance of the regional coordinated development strategy, 13 provinces/municipalities are expected to have a per capita gross domestic product (GDP) of more than US$15,000, and 16 provinces/municipalities will have a per capita GDP of US$10,000 15,000. All provincial economies are expected to achieve steady and rapid development by the end of the 14th FYP. 2) The total energy consumption of the provinces is expected to reach 5.45 Gtce (excluding Tibet) in 2025, and the average annual growth rate is approximately 1.5%. The growth of energy demand will remain in low speed. The key point of energy demand will gradually shift from the eastern to the middle area, while the proportion of energy use in the western provinces will remain stable, which is consistent with the economic development stage and regional coordinated development strategy. 3) The annual average carbon intensity (mainly considering carbon emissions from energy use) of the provinces will approximately with most provinces dropping by over 4.0%. The trend of a considerable decline in carbon intensity, as observed in recent years, is expected to continue.

Prediction of primary energy demand in China based on AGAEDE optimal model

In this article,we present an application of Adaptive Genetic Algorithm Energy Demand Estimation(AGAEDE) optimal model to improve the efficiency of energy demand prediction.The coefficients of the two forms of the model(both linear and quadratic) are optimized by AGA using factors,such as GDP,population,urbanization rate,and R&D inputs together with energy consumption structure,that affect demand.Since the spurious regression phenomenon occurs for a wide range of time series analysis in econometrics,we also discuss this problem for the current artificial intelligence model.The simulation results show that the proposed model is more accurate and reliable compared with other existing methods and the China's energy demand will be 5.23 billion TCE in 2020 according to the average results of the AGAEDE optimal model.Further discussion illustrates that there will be great pressure for China to fulfill the planned goal of controlling energy demand set in the National Energy Demand Project(2014—2020).

Scenario Formation of Energy Demand and CO_2 Emissionsfor Sustainable China

Co-integration theory has been employed in this paper and Granger causes are found between urbanization rate and GDP, between capital stock and GDP. Scenario analysis of GDP is performed using the GDP model established in the paper. The energy consumptions in Germany, Japan and other developed countries are analyzed and compared with the energy consumption in China. Environmental friendly scenario of energy demand and CO2 emissions for sustainable China has been formed based on the results of comparison. Under environmental friendly scenario, the primary energy consumption will be 4.31 billion ton coal equivalence （tee） and CO2 emissions will be 1.854 billion t-c in 2050; energy per capital will be 3.06 tee that is 1.8 times of energy consumed in 2005 in China and 51% of consumed energy per capital in Japan in 2003. In 2050, the energy requirement of unit GDP will be 20% lower than that of Germany in 2003, but will be still 37% higher than that in Japan in 2003. It is certain that to fulfill the environmental friendly Scenario of energy demand and CO2 emissions is a difficult task and it needs long term efforts of the whole society, not only in production sectors but also in service and household sectors,

Long-term Energy Demand and CO_2 Problem in the PRC

The long-term energy demand in China and the-Chinese share in global CO2 emission are forecasted on the basis of scenarios of population growth and economy development up to 2050 proposed in view of the interaction of energy, economy, environment and social development. The total energy demand in 2050 will reach 4.4~ 5.4 billion tce. It is shown in energy supply analysis that coal is China’s major energy in primary energy supply. The share of CO2 emission in the future Chinese energy system will be out of proportion to its energy consumption share because of the high persentage of coal to be consumed. It will reach about 27%. The nuclear option which would replace 30.7% of coal in the total primary energy supply will reduce the share by 9.8%. So the policy considerations on the future Chinese energy system is of great importance to the global CO2 issues.

Study on the Determinants of Energy Demand in China

Based on the modern economic theory and the characteristics of China's energy consumption, this paper analyzes the determinants of energy demand in China, builds up a China's energy demand model, and examines the long-run relationship between China's aggregate energy consumption and the main economic variables such as GDP by using the Johansen multivariate approach. It is found that there exists unique long-run relationship among the variables in the model over the sampling period. An error-correction model provides an appropriate framework for forecasting the short-run fluctuations in the aggregate demand of China.

Energy Demand Growth Rate to Slow Down

China’s energy demand growth rate is expected to slow down next year, with the government’s efforts to curb energy consumption intensive industries taking effect, executives from State oil and power companies said yesterday. Refined oil product consumption in China is likely

Forecasting China's primary energy demand based on an improved AI model

An improved energy demand forecasting model is built based on the autoregressive distributed lag(ARDL) bounds testing approach and an adaptive genetic algorithm(AGA) to obtain credible energy demand forecasting results. The ARDL bounds analysis is first employed to select the appropriate input variables of the energy demand model. After the existence of a cointegration relationship in the model is confirmed, the AGA is then employed to optimize the coefficients of both linear and quadratic forms with gross domestic product, economic structure, urbanization,and technological progress as the input variables. On the basis of historical annual data from1985 to 2015, the simulation results indicate that the proposed model has greater accuracy and reliability than conventional optimization methods. The predicted results of the proposed model also demonstrate that China will demand approximately 4.9, 5.6, and 6.1 billion standard tons of coal equivalent in 2020, 2025, and 2030, respectively.

Impact of Economic Transformation on Energy Demand in Southwest China and Forecast

"Economic transformation"has become the main path to promote China's social and economic development,and many regions have increased the importance and attention to"economic transformation",and the Southwest region is no exception.Many cities in Southwest China are developing new energy sources to promote economic development and economic transformation.Economic transformation and economic development in Southwest China are mutually influencing and interacting,while energy development in Southwest China and its local economic development are mutually promoting and influencing,so economic transformation also affects energy demand and development in Southwest China.The importance of economic transformation should be taken into consideration.

Game-theoretic Applications for Decision-making Behavior on the Energy Demand Side:a Systematic Review

As an essential characteristic of the smart grid,energy demand users are being transformed from passive roles to active decision-makers.To analyze their decision-making behaviors,game theory has been widely applied on the demand side.This paper focuses on the classification and in-depth analysis of recent studies that propose game-theoretic approaches for decision optimi-zation of multiple demand users.This analysis classifies scenarios into various game participant categories,in-cluding distributed energy prosumers,small-and mid-dle-sized users,and large energy consumers.The in-depth analysis of each scenario,covering non-cooperative game,cooperative game,Stackelberg game,Bayesian game,and evolutionary game,is conducted by analyzing market operation mechanisms,model assumptions/formulations,and solution methods.Based on a comprehensive review of such studies,it is concluded that game-theoretic appli-cations on the demand side can benefit both the grid and the users,e.g.,reductions in the peak-to-average ratios and energy costs of the users.The prospects for the ap-plications of game theory on the demand side are dis-cussed,including application scenarios and methodologies.The overview presented in this paper is expected to sup-port researchers in comprehending typical game-theoretic concepts,keeping with the latest research developments,and identifying new and innovative appli-cations for the energy demand side.Index Terms—Energy demand side,game theory,Game-theoretic application,demand response,deci-sion-making behavior.

Evaluation of building energy demand forecast models using multi-attribute decision making approach

With the existence of several conventional and advanced building thermal energy demand forecast models to improve the energy efficiency of buildings,it is hard to find an appropriate,convenient,and efficient model.Evaluations based on statistical indexes(MAE,RMSE,MAPE,etc.)that characterize the accuracy of the forecasts do not help in the identification of the efficient building thermal energy demand forecast tool since they do not reflect the efforts entailed in implementation of the forecast model,i.e.,data collection to production/use phase.Hence,this work presents a Gini Index based Measurement of Alternatives and Ranking according to COmpromise Solution(GI-MARCOS),a hybrid Multi Attribute Decision Making(MADM)approach for the identification of the most efficient building energy demand forecast tool.GI-MARCOS employs(i)GI based objective weight method:assigns meaningful objective weights to the attributes in four phases(1:pre-processing,2:implementation,3:post-processing,and 4:use phase)thereby avoiding unnecessary biases in the expert’s opinion on weights and applicable to domains where there is a lack of domain expertise,and(ii)MARCOS:provides a robust and reliable ranking of alternatives in a dynamic environment.A case study with three alternatives evaluated over three to six attributes in four phases of implementation(pre-processing,implementation,post-processing and use)reveals that the use of GI-MARCOS improved the accuracy of alternatives MLR and BM by 6%and 13%,respectively.Moreover,additional validations state that(i)MLR performs best in Phase 1 and 2,while ANN performs best in Phase 3 and 4 with BM providing a mediocre performance in all four phases,(ii)sensitivity analysis:provides robust ranking with interchange of weights across phases and attributes,and(iii)rank correlation:ranks produce by GI-MARCOS has a high correlation with GRA(0.999),COPRAS(0.9786),and ARAS(0.9775).

Factors affecting electric vehicle acceptance,energy demand and CO_(2)emissions in Pakistan

This work aims to investigate the factors accelerating electric vehicle(EV)acceptance at the consumer end in Pakistan and analyzes the implications for policymakers for a fast-track EV transition.The study further in-vestigates the high EV penetration scenario resulting from the technology acceptance model(TAM's 80%EV)and its impact on energy demand and CO_(2)emissions.The study design used a quantitative analysis method with the survey as an instrument for data collection regarding EV acceptance.The model under investigation was adapted from the famous Technology-Acceptance Models(TAMs)and modified with other significant predictors evidenced in the literature.Correlation and stepwise regression were performed with a multicollinearity check for model hypothesis testing.Out of six predictors,only four factors were significant in accelerating the EV transition.Financial policies were found to be highly significant,followed by environmental concern,facilitating conditions and perceived ease of use.The research then used exponential smoothing forecasts for transport demand and developed an EV penetration scenario based on modified TAM results.The results highlight the significant in-crease in transport demand and the opportunity for Pakistan to limit passenger transport emissions to 36.6 MT instead of 61.6 MT by 2040.

Urban Physics： Effect of the micro-climate on comfort, health and energy demand

The global trend towards urbanisation explains the growing interest in the study of the modification of the urban climate due to the heat island effect and global warming, and its impact on enersy use of buildings. Also urban comfort, health and durability, referring respectively to pedestrian wind/ thermal comfort, pollutant dispersion and wind-driven rain are of interest. Urban Physics is a well- established discipline, incorporating relevant branches of physics, environmental chemistry, aerodynamics, meteorolosy and statistics. Therefore, Urban Physics is well positioned to provide keycontributions to the current urban problems and challenges. The present paper addresses the role of Urban Physics in the study of wind comfort, thermal comfort, energy demand, pollutant dispersion and wind-driven rain. Furthermore, the three major research methods applied in Urban Physics, namely field experiments, wind tunnel experiments and numerical simulations are discussed. Case studies illustrate the current challenges and the relevant contributions of Urban Physics.

Demanding energy from carbon

Carbon is the central element driving the evolution of our human society towards prosperity over several historical stages.As for now,we are in a stage of blossoming sciences and technologies related to carbon materials,as a result of which our evergrowing energy demand has been largely satisfied.Yet,the expected rise of carbon energy consumption and the emerging environmental concerns have prevented us from being optimistic.To build a sufficiently powered future,we have been revolutionizing our ways of carbon energy utilization by discovering and designing new carbon structures,exploring and enhancing their unique physicochemical properties,and pursuing environmentally friendly strategies.Emerging structures such as graphene and sp-bonded C18 have allowed us to discover carbon’s promising properties such as energy storage and superconductivity,while green energy solutions such as fuel cells and CO2 reduction are working synergistically to purify the ecospheric carbon cycle.Therefore,this essay timely discusses related carbon sciences and technologies that have been the milestones shaping our energy consumption,based on which our energy future can be envisioned to be green and prosperous.

Investigation of availability,demand,targets,and development of renewable energy in 2017-2050:a case study in Indonesia

Abundant potential of renewable energy(RE)in Indonesia is predicted to replace conventional energy which continues to experience depletion year by year.However,until now,the use of RE has only reached 2%of the existing potential of 441.7 GW.The main overview of this work is to investigate the availability of RE that can be utilized for electricity generation in Indonesia.National energy demand and targets in the long run during the 2017-2050 period are also discussed.Besides,government policies in supporting RE development are considered in this work.The results show that the potential of RE in Indonesia can be utilized and might replace conventional energy for decades.The use of RE for electricity generation can be achieved by employing a government policy that supports the investor as the executor of RE development.The selling price of electricity generated from RE is cheaper than electricity generated from fossils;this makes economy is more affordable for people.Finally,the target set by the government for utilizing RE as the main energy in Indonesia can be done by implementing several policies for the RE development.Thus,greenhouse gas emissions and the use of petroleum fuels can be reduced.

Application of a probabilistic LHS-PAWN approach to assess building cooling energy demand uncertainties

A deterministic approach to building energy simulation risks the omission of real-world uncertainties leading to prediction errors.This paper highlights limitations of this approach by contrasting it with a probabilistic uncertainty/sensitivity simulation approach.Latin hypercube sampling(LHS)generates 15000 unique model configurations to assess the effects of weather,physical and operational uncertainties on the annual and peak cooling energy demands for a residential building which situated in a hot and dry climatic region.Probabilistic simulations predicted 0.22–2.17 and 0.45–1.62 times variation in annual and peak cooling energy demands,respectively,compared to deterministic simulation.A novel density-based global sensitivity analysis(SA),i.e.,PAWN,is adopted to identify dominant input uncertainties.Unlike traditional SA methods,PAWN allows simultaneous treatment of continuous and categorical inputs from a generic input-output sample.PAWN is favourable when computational resources are limited and model outputs are skewed or multi-modal.For annual and peak cooling demands,the effects of weather and operational parameters associated with airconditioner and window operation are much stronger than these of other parameters considered.Consequently,these parameters warrant greater attention during modelling and simulation stages.Bootstrapping and convergence analysis also confirm the validity of these results.

Rooftop photovoltaic （PV） halls： Achieving economic energy demand systems for industrial benefit via lowering

Industrial hails are characterized with their retatively high roof-to-floor ratio, which facilitates ready deployment of renewable energy generation, such as photovoltaic （PV） systems, on the rooftop. To promote deployment of renewable energy generation, feed-in tariff （FIT） higher than the electricity rate is available in many countries to subsidize the capital investment. FIT comes in different forms. For net FIT, in order to maximize the economic benefit, surplus electridty generation at each hour is desirable. One way to achieve surplus electricity generation is by increasing generation capacity, which is synonymous to higher capital investment. In fact, surplus electricity generation can also be achieved by lowering the energy demand of the building. This particularly the case for industrial hatls, which are usually subject to high energy demand for space conditioning in order to remove the excess heat gain due to the many power-intensive processes. Building energy performance simulation toots can be used to explore the different building design options that could lower the energy demand. In this paper, single-objective optimization on investment return will be deployed to study the cost effectiveness among different options in lowering energv demand. It Will-be demonstrated with a case study of a warehouse.

Changes in the Economic Structure and Trends in China’s Future Energy Demands

The scientific evaluation of trends in China's future energy demands is highly important.Using provincial-level panel data from 1995 to 2015,we studied the relationships between the economic aggregate,the development of energy-intensive industries and energy demand from the perspective of changes in the proportion of energy-intensive industries in the national economy.We find that economic aggregate affects energy demand through energy-intensive industries and that changes in the economic structure are the key factor for change in energy demand.This means that China's future energy demand will be much lower than that contained in forecasts that did not consider this factor.Comprehensively promoting green-tech development and strengthening the regulation of energy-extensive industries will be one of the key options for realizing China's objective of controlling total energy consumption.

A Predictive Nighttime Ventilation Algorithm to Reduce Energy Use and Peak Demand in an Office Building

The effect of two nighttime ventilation strategies on cooling and heating energy use is investigated for a prototype office building in several northern America climates, using hourly building energy simulation software （DOE2.1E）. The strategies include： scheduled-driven nighttime ventilation and a predictive method for nighttime ventilation. The maximum possible energy savings and peak demand reduction in each climate is analyzed as a function of ventilation rate, indoor-outdoor temperature difference, and building thermal mass. The results show that nighttime ventilation could save up to 32% cooling energy in an office building, while the total energy and peak demand savings for the fan and cooling is about 13% and 10%, respectively. Consequently, finding the optimal control parameters for the nighttime ventilation strategies is very important. The performance of the two strategies varies in different climates. The predictive nighttime ventilation worked better in weather conditions with fairly smooth transition from heating to cooling season.