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Two-stage capacity optimization approach of multi-energy system considering its optimal operation

Energy and AI 2020年第1期1卷 35-63页

作者： X.J.Luo lukumon o.oyedele olugbenga o.Akinade Anuoluwapo o.Ajayi Big Data Enterprise and Artificial Intelligence Laboratory(Big-DEAL) University of the West of England(UWE)Frenchay CampusBristolUnited Kingdom

With the depletion of fossil fuel and climate change,multi-energy systems have attracted widespread attention in ***-energy systems,fuelled by renewable energy,including solar and biomass energy,are gain-ing increasing adoption in commercial *** of previous capacity design approaches are formulated based upon conventional operating schedules,which result in inappropriate design capacities and ineffective operating schedules of the multi-energy ***,a two-stage capacity optimization approach is pro-posed for the multi-energy system with its optimal operating schedule taken into *** demonstrate the effectiveness of the proposed capacity optimization approach,it is tested on a renewable energy fuelled multi-energy system in a commercial *** primary energy devices of the multi-energy system consist of biomass gasification-based power generation unit,heat recovery unit,heat exchanger,absorption chiller,elec-tric chiller,biomass boiler,building integrated photovoltaic and photovoltaic thermal hybrid solar *** variable efficiency owing to weather condition and part-load operation is also *** algorithm is adopted to determine the optimal design capacity and operating capacity of energy devices for the first-stage and second-stage optimization,*** two optimization stages are interrelated;thus,the optimal design and operation of the multi-energy system can be obtained simultaneously and *** the adoption of the proposed novel capacity optimization approach,there is a 14%reduction of year-round biomass consumption compared to one with the conventional capacity design approach.

关键词： Multi-energy system Renewable energy Biomass Genetic algorithm Capacity design optimization

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Genetic algorithm-determined deep feedforward neural network architecture for predicting electricity consumption in real buildings

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Energy and AI 2020年第2期2卷 83-100页

作者： X.J.Luo lukumon o.oyedele Anuoluwapo o.Ajayi olugbenga o.Akinade Juan Manuel Davila Delgado Hakeem A.owolabi Ashraf Ahmed Big Data Enterprise and Artificial Intelligence Laboratory(Big-DEAL) University of the West of England(UWE)Frenchay CampusBristolUnited Kingdom Department of Civil and Environmental Engineering Brunel University LondonLondonUnited Kindom

A genetic algorithm-determined deep feedforward neural network architecture(GA-DFNN)is proposed for both day-ahead hourly and week-ahead daily electricity consumption of a real-world campus building in the United *** to the comprehensive relationship between affecting factors and real-world building electricity consumption,the adoption of multiple hidden layers in the deep neural network(DFNN)algorithm would improve its prediction *** architecture of a DFNN model mainly refers to its quantity of hidden layers,quantity of neurons in the hidden layers,activation function in each layer and learning process to obtain the connecting *** optimal architecture of DFNN model was generally determined through a trial-and-error process,which is an exponential combinatorial problem and a tedious *** address this problem,genetic algorithm(GA)is adopted to automatically design an optimal architecture with improved generalization *** year and six months of measurement data from a campus building is used for training and testing the proposed GA-DFNN model,*** demonstrate the effectiveness of the proposed GA-DFNN prediction model,its prediction performance,including mean absolute percentage error,coefficient of determination,root mean square error and mean absolute error,was compared to the reference feedforward neural network models with single hidden layer,DFNN models with other architecture,random search determined DFNN model,long-short-term-memory model and temporal convolutional network *** comparison results show that the proposed GA-DFNN predictive model has superior performance than all the reference prediction models,demonstrating the optimization effectiveness of GA and the prediction effectiveness of DFNN model with multiple hidden layers and optimal architecture.

关键词： Prediction Deep learning Feedforward neural network Genetic algorithm Electricity consumption

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Achieving Sustainable Development in Small Communities via Combined Heat and Power Systems

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Engineering（科研） 2012年第3期4卷 160-169页

作者： olaosebikan A. olafadehan lukumon Salami Babatunde K. Adeoye Toluwalase o. Ajayi Department of Chemical and Polymer Engineering Lagos State University Lagos Nigeria Department of Chemical Engineering University of Lagos Lagos Nigeria

The design of a feasible Combined Heat and Power plant for a small community is presented. of the many alternatives solid-waste disposal methods available, incineration of solid waste is recommended due to its potential energy recovery of the heat released during solid waste incineration and recovery of valuable by-products that can either be reused, re- cycled or marketed, among other advantages. Hence, an attempt is made to use the heat energy released during incin- eration of solid waste to produce steam in a boiler, which in turn powers a turbine for eventual generation of electricity. The two processes involved in the generation of electricity for in-plant use or for a small community via a steam turbine-generator combination and a gas compressor-gas turbine-generator are presented. The analysis of the amount of energy produced from the solid waste energy-conversion system using an incinerator-boiler-steam turbine-electric generator combination with a capacity of 4.5 tons/day is also presented. The net electric power for a small community was found to be 148.24 kW with an overall efficiency of about 21% having taken cognisance of the process power needs and unaccounted process heat losses. Moreover, exergy analysis of the proposed CHP plant was carried out whereby the respective energy and exergy efficiencies of 83.2% and 62.1% were obtained.

关键词： Sustainable Development Solid Waste Incineration Boiler Turbine Electric Generator CHP Efficiency Exergy

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