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Integrated Multi-Head Self-Attention Transformer model for electricity demand prediction incorporating local climate variables

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Energy and AI 2023年第4期14卷 620-644页

作者： sujan ghimire Thong Nguyen-Huy Mohanad S.AL-Musaylh Ravinesh C.Deo David Casillas-Perez Sancho Salcedo-Sanz School of Mathematics Physics and ComputingUniversity of Southern QueenslandSpringfieldQLD4300Australia Centre for Applied Climate Sciences University of Southern QueenslandToowoomba4350QLDAustralia Department of Information Technologies Management Technical CollegeSouthern Technical UniversityBasrah 61001Iraq Department of Signal Processing and Communications Universidad Rey Juan CarlosFuenlabrada28942MadridSpain Department of Signal Processing and Communications Universidad de AlcaláAlcaláde Henares28805MadridSpain

This paper develops a trustworthy deep learning model that considers electricity demand(G)and local climate *** model utilises Multi-Head Self-Attention Transformer(TNET)to capture critical information from𝐻,to attain reliable predictions with local climate(rainfall,radiation,humidity,evaporation,and maximum and minimum temperatures)data from Energex substations in Queensland,*** TNET model is then evaluated with deep learning models(Long-Short Term Memory LSTM,Bidirectional LSTM BILSTM,Gated Recurrent Unit GRU,Convolutional Neural Networks CNN,and Deep Neural Network DNN)based on robust model assessment *** Kernel Density Estimation method is used to generate the prediction interval(PI)of electricity demand forecasts and derive probability metrics and results to show the developed TNET model is accurate for all the *** study concludes that the proposed TNET model is a reliable electricity demand predictive tool that has high accuracy and low predictive errors and could be employed as a stratagem by demand modellers and energy policy-makers who wish to incorporate climatic factors into electricity demand patterns and develop national energy market insights and analysis systems.

关键词： Electricity demand forecasting Sustainable energy Artificial Intelligence Deep learning Transformer Networks Kernel Density Estimation

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Increase of forest carbon biomass due to community forestry management in Nepal

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Journal of Forestry Research 2018年第2期29卷 424-433页

作者： Anup K C Roshani Manandhar Rajeshor Paudel sujan ghimire Department of Environmental Science Amrit CampusTribhuvan University Central Department of Environmental Science Tribhuvan University Department of Environmental Science Tri-Chandra CampusTribhuvan University

Community forest management helps in mitigating deforestation and forest degradation by addressing the negative aspects of rural livelihoods such as poverty and social *** is important in regulating global climate by encouraging sequestration of carbon in shoots,roots and *** studied the status of community forest management,forest resource harvest and carbon stocks in two community forests of the mid hill region of central and western *** study was based on primary and secondary data collected through carbon stock measurement from field visits and allometric equations,household surveys,focus group discussions,key informant interviews,and review of past *** variables such as gender,age group,livestock and landholding status were related to resource utilization,conservation,and management of community *** resources such as timber,firewood,fodder and leaf litter were harvested in sustainable *** were involved in forest thinning,co-management meetings,guarding and planting trees for forest conservation and *** and carbon stock of trees increased gradually in comparison to a previous *** recommend further research on other community forests for more accurate and better results.

关键词： Community forest management Resource use Carbon stock Nepal

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