Long-term Energy Demand Forecasting Research Articles

Long-term evolution of energy and electricity demand forecasting: The case of Ethiopia

Long-term energy demand forecasting is crucial for any country, in particular for developing countries with rapid developments of energy needs. This study focuses on Ethiopia, a country with a highly increasing energy demand resulting mainly from the currently low share of electricity access, rapid development of industrial parks, extensive expansion of the railway network, extensive irrigation schemes for agriculture, new cement and sugar factories, housing projects, power export plan to neighboring countries, etc. These all are on top of the 2.7% average population growth. In this study, the Long-range Energy Alternatives Planning System (LEAP) is used to explore different possible futures and also to forecast the long-term energy requirements in Ethiopia. The planning period is 33 years from 2018 to 2050. The study employs six different scenarios to unfold the future evolution. The developed scenarios are Business-As-Usual (BAU), Growth in Electrification and Urbanization (E&U), High Economic Growth (HEG) and three policy-driven, Improved Energy Efficiency (IEE-1, IEE-2 and IEE-3) scenarios. The pathways represented by these scenarios can show the maximum expected rise in demand under different drivers and the best-case energy saving opportunities. The model is also used to estimate the associated greenhouse gas (GHG) emissions.

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.

Ethiopian energy status and demand scenarios: Prospects to improve energy efficiency and mitigate GHG emissions

The energy sector of Ethiopia continues to largely rely on traditional biomass energy due to limited access to modern energy sources to meet growing demand. Long-term energy demand forecasting is essential to guide the country's plans to expand the energy supply system. This study provides a general overview of Ethiopia's current energy demand and forecasts sector-wise energy demand out to 2030 for alternative policy scenarios using the Long-range Energy Alternative Planning (LEAP) model. The reference scenario assumes a continuation of recent energy consumption trends and takes account of current energy and economic dynamics. Three alternative scenarios on improved cookstoves, efficient lighting, and universal electrification scenario were identified as key priorities of the government of Ethiopia and modeled. Results from the model can assist energy planners in ensuring that the country's capacity for supply meets projected growth in demand for energy. They also shed light on the tradeoffs implicit in alternative policy priorities and investments in terms of economic development and environmental sustainability. Most importantly, the results suggest that alternative investments can conserve energy, improve environmental sustainability, enhance energy equity and improve the country's development indicators.

Mid-Term Energy Demand Forecasting by Hybrid Neuro-Fuzzy Models

This paper proposes a structure for long-term energy demand forecasting. The proposed hybrid approach, called HPLLNF, uses the local linear neuro-fuzzy (LLNF) model as the forecaster and utilizes the Hodrick–Prescott (HP) filter for extraction of the trend and cyclic components of the energy demand series. Besides, the sophisticated technique of mutual information (MI) is employed to select the most relevant input features with least possible redundancies for the forecast model. Each generated component by the HP filter is then modeled through an LLNF model. Starting from an optimal least square estimation, the local linear model tree (LOLIMOT) learning algorithm increases the complexity of the LLNF model as long as its performance is improved. The proposed HPLLNF model with MI-based input selection is applied to the problem of long-term energy forecasting in three different case studies, including forecasting of the gasoline, crude oil and natural gas demand over the next 12 months. The obtained forecasting results reveal the noteworthy performance of the proposed approach for long-term energy demand forecasting applications.

Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025

The energy supply and demand should be closely monitored and revised the forecasts to take account of the progress of liberalization, energy efficiency improvements, structural changes in industry and other major factors. Medium and long-term forecasting of energy demand, which is based on realistic indicators, is a prerequisite to become an industrialized country and to have high living standards. Energy planning is not possible without a reasonable knowledge of past and present energy consumption and likely future demands. Energy demand management activities should bring the demand and supply closer to a perceived optimum. Turkey's energy demand has grown rapidly almost every year and is expected to continue growing. However, the energy demand forecasts prepared by the Turkey Ministry of Energy and Natural Resources overestimate the demand. Recently many studies are performed by researchers to forecast the energy demand of Turkey. Particle swarm optimization (PSO) technique has never been used for such a study. In this study a model is proposed, using PSO-based energy demand forecasting (PSOEDF), to forecast the energy demand of Turkey more efficiently. Although there are other indicators as well, gross domestic product (GDP), population, import and export are used as basic energy indicators of energy demand. In order to show the accuracy of the algorithm, a comparison is made with the ant colony optimization (ACO) energy demand estimation model which is developed for the same problem.

Energy Demand Forecasting by Thermodynamic Analysis of Energy Consumed Processes

The article presents a method of energy demand forecasting by using thermodynamic analysis of processes in the energy consumption sphere. Energy demand of a branch is determined on the base of modeling the energy consumption of an individual process by its material and energy balances. It makes it possible to take into account improvement of existing processes, change of technologies and introduction of energy conservation measures when the specific energy consumption for the product is determined for short- and mid-term forecasting. It is suggested to apply forecasting of energy and exergy efficiencies of technologies by long-term forecasting of energy demand. The complete energy balance method and concepts of the chemical energy and exergy of feedstock, ideal and idealized analogues of real processes, minimum necessary and limiting energy and work consumption are used.

Risks, predictions and other optical illusions: Rethinking the use of science in social decision-making

This article examines two examples of what has been called mandated science - risk analysis and long-term energy demand forecasting - with a view to disentangling the implicit assumptions underlying their use in public policy debates and in the policy process. In both fields, there has emerged a growing and very similar challenge to traditional views of the role and status of mandated science in informing decision-making. This challenge has led to the reformulation of some of the attributes of the standard view, without however causing changes in the underlying view of the nature of the problem or the role of science in principle. A deeper critique suggests that mandated science is inherently value-laden, producing conclusions that are strongly affected by theoretical presuppositions that in turn reflect underlying values (e.g., about rationality, knowledge or the role of technology). Thus a sharp separation of science and policy is untenable and amounts to a hidden policy bias in favor of the business-as-usual policies supported by conventional science. There is a need for a new concept of science, one that is more integrated into the policy context, more contextual and openly value-laden, less oriented to mastery over natural and social processes, and more accessible to the public at large.