Allan Rivalles Souza Feitosa
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Henrique Figuerôa Lacerda
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Wellington Pinheiro dos Santos
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Abel Guilhermino da Silva Filho
Accelerated population growth in the 21st century and increased demand for energy sources, associated with climate change, have resulted in two main challenges: the search for sustainable energy sources and the need to find more efficient ways to use extant sustainable sources. The forecasting module provides an estimate of the future usage of these appliances and it is the source of the recommended module’s suggestion. Time Series Forecasting techniques, such as Autoregressive Integrated Moving Average, LongShort Term Memory (LSTM), Gated Recurrent Units, Echo State Networks (ESN), and Support Vector Regression, were tested for the predictive module. Multiobjective optimization techniques such as NonSorted Genetic Algorithm II (NSGA II), MultiObjective Particle Swarm Optimization (MOPSO), Speed constrained Multi-objective Particle Swarm Optimization (SMOPSO), and Strength Pareto Evolutionary Algorithm two (SPEA2), for example, were tested for the Recommendation Module. The Forecasting and Recommendation module experiments were performed independently. In the Forecasting Module, the results and statistical tests revealed LSTM as the best suited technique for forecasting the loads of the majority of the appliances tested (in this case seven) in terms of root mean square error. In the experiments performed for the recommendation module, NSGA II showed a higher overall performance compared to other metrics in terms of hyper volume of the Pareto Front generated. This work presents the potential of using both Predictive Models and MultiObjective Optimization Techniques combined to reduce energy usage in household environments.
Identification of monthly municipal water demand system based on autoregressive integrated moving average model tuned by particle swarm optimization