Forecasting the Energy Consumption in Afghanistan with the ARIMA Model

Mitkov, Atanas; Noorzad, Najmuddin; Gabrovska-Evstatieva, Katerina; Mihailov, N.

doi:10.1109/elma.2019.8771680

Cited by 13 publications

(6 citation statements)

References 5 publications

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“…The accurate forecasting of energy consumption is essential to the continued growth of Afghanistan's economy and society over the long term. 74 Nepal and Bhutan are primarily reliant on traditional energy sources including firewood, agricultural leftovers, and animal dung, as shown in Figures 11(a) and 14(d), due to the lack of significant local deposits of fossil fuels. 75,76 Population growth, industrialization, and economic growth have boosted Nepal and Bhutan's energy usage.…”

Section: Discussionmentioning

confidence: 99%

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Assessment of SAARC nations' solar energy potential for sustainable development

Mittal

2022

Energy & Environment

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Energy is very vital for the economic development and prosperity of any nation. Expanding a country's use of renewable energy sources can help it meet its current and future energy needs, as well as spur economic growth. Sustainable energy supply, electrification rate, population shifts, and per-capita power use affect economic development and prosperity. Electricity use is a key sign of a country's economic growth, and as the economy grows, so does the need for energy. Electricity generation relies on the availability of primary sources, implementation of technology as well as on policy imperatives. The South Asian Association for Regional Cooperation is the regional intergovernmental organization and geopolitical union of states in South Asia. Its member states are India, Afghanistan, Bhutan, Bangladesh, Nepal, Maldives, Sri Lanka, and Pakistan. SAARC countries are wealthy in natural resources, yet economic growth is a difficulty. This study aims to assess the solar energy potential of SAARC countries, which has received little attention. Prior research on solar energy possibilities in SAARC concentrated on one country or group. According to the study, SAARC countries rely heavily on imported fossil fuels and a single energy source. Renewable energy sources, especially solar energy, are important for their sustainable expansion, diversity of energy basket, energy security, and fulfilling rising electricity demand. The SAARC region has superior daily average horizontal sun irradiances than the UK and Germany, indicating untapped potential for solar photovoltaic systems. This study aims to encourage stakeholders to execute efficient solar energy governance planning for sustainable growth.

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Section: Discussionmentioning

confidence: 99%

“…The accurate forecasting of energy consumption is essential to the continued growth of Afghanistan's economy and society over the long term. 74…”

Section: Discussionmentioning

confidence: 99%

Assessment of SAARC nations' solar energy potential for sustainable development

Mittal

2022

Energy & Environment

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“…Furthermore, Eerdogdu (2007) also used cointegratison analysis with ARIMA to predict total energy consumption in Turkey, while ) used ARIMA models to predict agricultural loads at small scales. Other similar efforts include predicting energy consumption in Morocco (Kafazi et al, 2016), Ghana (Sarkodie, 2017), Afghanistan (Mitkov et al, 2019), India, China, and the USA (Jiang et al, 2018), and Middle Africa (Wang et al, 2018). In the case of using GARCH for prediction purposes in the energy sector, the primary use cases of this method if predicting the volatility of the energy market and load forecasting.…”

Section: Energy Demand/consumption Forecastingmentioning

confidence: 99%

Incorporating causality in energy consumption forecasting using deep neural networks

2022

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Forecasting energy demand has been a critical process in various decision support systems regarding consumption planning, distribution strategies, and energy policies. Traditionally, forecasting energy consumption or demand methods included trend analyses, regression, and auto-regression. With advancements in machine learning methods, algorithms such as support vector machines, artificial neural networks, and random forests became prevalent. In recent times, with an unprecedented improvement in computing capabilities, deep learning algorithms are increasingly used to forecast energy consumption/demand. In this contribution, a relatively novel approach is employed to use long-term memory. Weather data was used to forecast the energy consumption from three datasets, with an additional piece of information in the deep learning architecture. This additional information carries the causal relationships between the weather indicators and energy consumption. This architecture with the causal information is termed as entangled long short term memory. The results show that the entangled long short term memory outperforms the state-of-the-art deep learning architecture (bidirectional long short term memory). The theoretical and practical implications of these results are discussed in terms of decision-making and energy management systems.

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“…O modelo proposto possui uma melhoria para a função de ativação da rede de previsão, onde a não linearidade produz efeitos diretamente na janela temporal. Comparandose com os trabalhos feitos por Liu and Lu (2011), Makhloufi et al (2018), Mitkov et al (2019) e Lukhyswara et al (2019), a estimação dos parâmetros de histerese na metodologia realizada neste trabalhoé feita diretamente nos dados. Desta forma,é apresentado uma sensibilidade no aprendizado, o que beneficia o reconhecimento das amplitudes de dados, com exceção de picos.…”

Section: Conclusãounclassified

Predição Solar e Eólica Utilizando Redes com Neurônios de Histerese L2P para Aplicações em Smart Grids

Jesus

Pontes

Borges

et al. 2020

Anais Do Simpósio Brasileiro De Sistemas Elétricos 2020

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This paper focuses on the study of the strategic prediction of renewable sources of intermittent energy, using bio-inspired computational models, developed in Python, with the gaim of providing mechanisms that help in the monitoring and control of smart grids. To perform the learning of the neural network, we used the Backpropagation and Feedforwad algorithms. This neural network makes use of hysteresis neurons through the L 2 P model that, therefore, iterates the data to reproduce the prediction curves. To evaluate the model, real data obtained from the National Institute of Meteorology (INMET) was used. Results are presented through the application of the L 2 P model as a neural network and compared to existing structures in neural networks such as the ARIMA method, showing the good performance of the L 2 P neural network. Resumo: Este artigo concentra-se no estudo da predição estratégica de fontes renováveis de energia intermitente, utilizando modelos computacionais baseados em redes neurais artificiais (RNAs), desenvolvidos em linguagem Python, com o intuito de fornecer mecanismos que auxiliem no monitoramento e controle das redes elétricas inteligentes. Para a realização do aprendizado da rede neural, utilizou-se os algoritmos Backpropagation e Feedforwad. A rede neural em questão, faz o uso de neurônios de histerese por meio do modelo L 2 P (Limity Loop Proximity) que, portanto, realiza a iteração dos dados para reproduzir as curvas de previsão de radiação solar e velocidade do vento. Para avaliar o modelo, empregou-se dados reais obtidos no Instituto Nacional de Meteorologia (INMET). Resultados são apresentados por meio da aplicação do modelo L 2 P como rede neural e comparado a estruturas já existentes em redes neurais como o método ARIMA, evidenciando o bom desempenho da rede L 2 P .

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Forecasting the Energy Consumption in Afghanistan with the ARIMA Model

Cited by 13 publications

References 5 publications

Assessment of SAARC nations' solar energy potential for sustainable development

Assessment of SAARC nations' solar energy potential for sustainable development

Incorporating causality in energy consumption forecasting using deep neural networks

Predição Solar e Eólica Utilizando Redes com Neurônios de Histerese L2P para Aplicações em Smart Grids

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