Abstract
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.
Highlights
The accelerated population growth of the 21st century and the resulting demand for more energy sources associated with climate change have brought two main challenges: first, the need to develop greener electricity sources that do not harm the environment and, at the same time, meet the main demands, and second, the need to manage efficiently energy consumption by avoiding electricity wastage and stimulating more conscious energy usage
Despite the loss of information resulting from the aggregations, usage recommendations on a second by second basis might be too invasive and not easy to understand from the point of view of the user
The choice of Long--short Term Memory (LSTM), Echo State Networks (ESN), or Gated Recurrent Unit (GRU), and a window of two weeks of past data as the only prediction for all the appliances would cover most of the scenarios with reasonable performance
Summary
The accelerated population growth of the 21st century and the resulting demand for more energy sources associated with climate change have brought two main challenges: first, the need to develop greener electricity sources that do not harm the environment and, at the same time, meet the main demands, and second, the need to manage efficiently energy consumption by avoiding electricity wastage and stimulating more conscious energy usage. In 2018, electricity generation was responsible for 38% of the global energy -related CO2 emissions. This represented a growth of 4% relative to 2017. The increasing demand is a result of climate change such as warmer summers and the consequent increased reliance on air conditioning, as well as colder winters and the need for increased heating in specific regions of the world (International Energy Agency, 2019).
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