# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 3 Energy Management
### Energy Prediction  
Selecting an appropriate source of energy is crucial in extraterrestrial missions. Yet another useful strategy in energy management is adjusting energy expenditure in accordance with energy availability. For instance, in systems powered only by electrochemical batteries, measuring energy availability can be as simple as measuring the battery's open circuit voltage. This measurement, combined with the certainty that energy does not inherently increase in a battery over time (unless recharged), provides us with the maximum energy available in a system for its future operations. Combining this knowledge with the energy consumed by various tasks of the system can help in formulating an apt strategy for task execution, thus achieving considerate management of the available energy. However, the complexity in measuring the maximum amount of available energy for future operations of the system increases when the energy in the batteries can be replenished from ambient sources such as solar, thermal, or wind power, due to the uncertainty in the ambient source's availability. For instance, the uncertainty in solar, thermal, and wind power is introduced by factors such as the relative position and orientation of the system with respect to the energy source, solar activity, seasonal effects, and the weather conditions of the atmosphere containing the system. This uncertainty means that the maximum amount of available energy can change in the future from its present value in a non-linear fashion. Energy harvesting systems draw the required energy from ambient sources. As energy harvesting systems depend on these sources, the success of a task in terms of executing before the deadline with sufficient energy can depend on the accuracy of the estimations of available future energy [40]. Energy harvesting allows systems to be designed with reduced battery sizes or even replace batteries with enduring capacitors (i.e., battery-less systems) for improved sustainability. Multiple works have been conducted to predict the energy available for energy harvesting systems on Earth [41; 42]. These works include statistical models, such as auto-regressive integrated moving average (ARIMA), exponentially weighted moving average (EWMA), weather conditioned moving average (WCMA), profile energy prediction (Pro-Energy), and machine learning (ML) algorithms such as neural networks, Q-learning, and random forests. Depending on when in the future the accumulated energy value needs to be predicted, the prediction horizon can range from seconds to days [42]. A more sophisticated forecasting algorithm is required for accurate prediction when the prediction horizon is distant, as simple models such as ARIMA tend to have prediction errors that are approximately proportional to the prediction horizon. While the above techniques have been applied in terrestrial environments, similar prediction models can also be applied on Mars, as illustrated in Figure 3. Martian environments have two significant conditions, namely, clear sky and dust stormy sky. However, the limited availability of Martian meteorological data and the extreme weather conditions on the planet make designing such time series forecasting models challenging. Synthetic data generation and online learning forecasting models can be used to alleviate this problem [43]. [PERSON] et al. describe how such generated data can both enrich existing Martian datasets with additional synthetic samples based on actual observations, as well as augment datasets for which samples are scarce with generated examples that approximate those on Mars [44]. These models can help Martian vehicles anticipate the quantity of available energy and accordingly adjust the current energy expense to maintain the reserve energy. The synthetic dataset can build upon the years of Martian meteorological results collected by the Mars Climate Database [45].