Optimisation of the Global Calculator
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Sample optimisation constraints
Monte Carlo Markov Chain analysis
Genetic algorithms optimiser
Covariance Matrix Adaption Evolutionary Strategy analysis
Artificial Neural Networks analysis
Optimisation of the Global Calculator via Genetic Algorithms
Set-up
Creating a new generation
Fitness function
Selection
Mutation
Crossover
Enabling multiple constraints
Defining the optimisation constraints
Optimisation
Iterations
Cost minimisation
Optimisation of the Global Calculator via Monte Carlo Markov Chains
Set-up
Temperature sensitivity analysis
Cost sensitivity analysis
Generalising MCMC (2 constraints) to all levers
Unbounded prior for all levers
Generating observations
Defining a likelihood function
Running MCMC and logging results
Loading pre-computed results (24-hours long Markov Chain)
Correlation matrix of accepted MCMC lever combinations
Paired density and scatter plot matrix of lever combinations accepted by MCMC
Correlation matrix of output values
Export correlation data to GEPHI
Posterior distribution of accepted MCMC lever combinations
Posterior distribution of model outputs accepted by MCMC
Evolution of temperature values
Evolution of cost values
Acceptance rate
Autocorrelation function of accepted model outputs
Summary of inputs to the Global Calculator
License
Requirements
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Requirements
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Source
Requirements
ΒΆ
numpy==1.19.1
pandas==1.1.0
matplotlib==3.3.1
torch==1.6.0
selenium==4.0.0