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

Gebremeskel, Dawit; Ahlgren, Erik O.; Beyene, Getachew Bekele

doi:10.1016/j.esr.2021.100671

Cited by 37 publications

(9 citation statements)

References 27 publications

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“…The result has been more efficient demand prediction, although the incorporation of price prediction has often been elusive. Other countries using ANN in consumption prediction include Ghana, Ethiopia, Tanzania, Rwanda, and Angola [30][31][32]. One of the main challenges of nonlinear energy prediction models such as ANN is the lack of end-user incorporation, especially in long-term predictions [32].…”

Section: Using the Artificial Neural Network Methodsmentioning

confidence: 99%

“…Other countries using ANN in consumption prediction include Ghana, Ethiopia, Tanzania, Rwanda, and Angola [30][31][32]. One of the main challenges of nonlinear energy prediction models such as ANN is the lack of end-user incorporation, especially in long-term predictions [32]. Predictions of elements such as growth in demand and effects on production cost are increasingly challenging because of the expert approach rather than a participatory approach.…”

Section: Using the Artificial Neural Network Methodsmentioning

confidence: 99%

“…Most of these models have also been used long enough within the companies to create familiarity and ample understanding. However, the results showed common weaknesses for the models, including lack of consumer participation [25], inadequate end user prediction and load challenges [32], and lack of proper consideration of externalities [42]. These challenges not only make pricing harder but also create more volatility in the market.…”

Section: Commonly Used Energy Pricing Models In Ssa and The Existing Gapmentioning

confidence: 99%

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Energy price modeling in sub-Saharan Africa: a systematic literature review

Luhangala

Ambole

Musango

et al. 2022

Environ. Res.: Infrastruct. Sustain.

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The energy market in Sub-Saharan Africa (SSA) is not meeting the demands of the region’s growing population. Energy access remains a significant challenge, with most people on the continent still reliant on biomass and other traditional forms of energy. Paradoxically, research has found that the African continent has the highest potential for renewable energy generation. For this energy to be commercialized effectively, there is a need to understand energy price modelling in the SSA context. Our initial review of Literature showed that energy price modelling has received little attention in SSA. This paper, therefore, fills this gap by using a systematic literature review to consolidate knowledge on how energy price modelling has been applied in the SSA context. The systematic literature review results reveal four commonly used models: time series, Artificial Neural Network, Hybrid Iterative Reactive Adaptive (HIRA), and Hybrid models. Across the 46 SSA countries, governments have applied these models to price electricity and petroleum at the national level. However, these models have not been applied to renewable energy markets. Neither have they been applied at the household level. In the discussion, we hypothesize that price modelling can be used at the household level to improve energy decision-making. For this to work, price modelling should be simplified, user-friendly, and accessible to households. In conclusion, we recommend that SSA governments develop a more holistic view of energy price modelling to better harness the potential of renewables. They can do this through effective stakeholder engagement that includes the needs of small businesses and households. The main lessons drawn from this review include the possibility of using energy price modelling technology as a pathway to encouraging energy transitions to renewable energy in informal settlements in Africa. Using technology to bring the price modelling closer to the people is also an important element in facilitating effective transition to renewable energy. Finally, including the members of the community in pricing through creation of awareness on the models used and popularizing technology that can help in predictive pricing will help in creating better and faster energy transitions.

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Section: Using the Artificial Neural Network Methodsmentioning

confidence: 99%

Section: Using the Artificial Neural Network Methodsmentioning

confidence: 99%

Section: Commonly Used Energy Pricing Models In Ssa and The Existing Gapmentioning

confidence: 99%

See 1 more Smart Citation

Energy price modeling in sub-Saharan Africa: a systematic literature review

Luhangala

Ambole

Musango

et al. 2022

Environ. Res.: Infrastruct. Sustain.

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“…In [34], a long-term energy alternatives planning system is used to explore different possible futures and also to forecast the long-term energy requirements in Ethiopia in a period of 33 years. A hybrid DL model based on stacked LSTM network and genetic algorithm is proposed in [35].…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity supply chain hybrid long‐term demand forecasting approach based on deep learning—a case study of basic metals industry

Moalem

Ahari

Shahgholian

et al. 2023

The Journal of Engineering

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Demand forecasting is a key parameter to achieve optimal supply chain management at different levels. The basic metals industry is one of the most energy‐intensive industries in the electricity supply chain. The impossibility of large‐scale energy storage, reservation constraints, and its high costs, limitations on electricity transmission lines capacity, real‐time response to high priority strategic demands, and variety of energy rates at different hours and seasons are issues that challenge the electricity supply chain. A hybrid approach is presented in this paper to improve the accuracy of long‐term demand forecasting in the electricity supply chain. The proposed approach uses wavelet decomposition and long short‐term memory (LSTM) neural networks to produce new predictors for demand forecasting in case of defective data. The data used in this study consists of the recorded hourly demand of Espidan Iron Stone (EIS) company and data of Mobarakeh Steel (MS) company in Isfahan Province. The understudy time series includes a lot of interruptions due to non‐production of the factory or power outages and spikes which cause uncertainty that makes the forecasting more difficult in comparison with continuous time series. Multiple machine learning models including decision trees (DTs), boosted and bagged trees (BGTs), support vector regression (SVR) models, extreme learning machines (ELMs), and a LSTM neural network model are employed to evaluate the effectiveness of the proposed approach in this paper. A comparison of the results obtained using the hybrid proposed approach and training machine learning (ML) methods shows a notable reduction in forecasting error.

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“…Hydropower accounts for 89 percent of total electricity generation, with a total capacity of 4284 MW [37]. Distinct energy-related concerns in Ethiopia were investigated in a variety of studies with various goals [38]. No studies have been conducted to evaluate electric vehicle charging stations that use hybrid energy sources.…”

Section: Introductionmentioning

confidence: 99%

Feasibility and Techno-Economic Analysis of Electric Vehicle Charging of PV/Wind/Diesel/Battery Hybrid Energy System with Different Battery Technology

Muna

Kuo

2022

Energies

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Promoting the development of green technologies and replacing fossil fuel vehicles with electric ones can abate the environmental anxieties and issues associated with energy supply security. The increasing demand for electric vehicles requires an upgrade and expansion of the available charging infrastructure to accommodate the fast public adoption of this type of transportation. Ethiopia set a pro-electric cars policy and made them excise-free even before the first electric vehicle charging stations were launched by Marathon Motors Engineering in 2021. This paper presents the first ever technical, economic and environmental evaluation of electric vehicle charging stations powered by hybrid intermittent generation systems in three cities in Ethiopia. This paper tests this model using three different battery types: Lead-acid (LA), Flow-Zince-Bromine (ZnBr) and Lithium-ion (LI), used individually. Using these three battery technologies, the proposed hybrid systems are then compared in terms of system sizing, economy, technical performance and environmental stability. The results show that the feasible configuration of Solar Photovoltaic (PV)/Diesel Generator (DG)/ZnBr battery systems provide the lowest net present cost (NPC), with values of $2.97M, $2.72M and $2.85M, and cost of energy (COE), with values $0.196, $0.18 and $0.188, in Addis Ababa, Jijiga and Bahir Dar, respectively. Of all feasible systems, the Wind Turbine (WT)/PV/LI, PV/LI and WT/PV/LI configurations have the highest values of NPC and COE in Addis Ababa, Jijiga and Bahir Dar. Using this configuration, the results demonstrate that ZnBr battery is the most favorable choice because the economic parameters, including total NPC and COE, are found to be lowest.

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

Cited by 37 publications

References 27 publications

Energy price modeling in sub-Saharan Africa: a systematic literature review

Energy price modeling in sub-Saharan Africa: a systematic literature review

Electricity supply chain hybrid long‐term demand forecasting approach based on deep learning—a case study of basic metals industry

Feasibility and Techno-Economic Analysis of Electric Vehicle Charging of PV/Wind/Diesel/Battery Hybrid Energy System with Different Battery Technology

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