Generally, the exact load demand for a tug is not exactly known. However, based on the port where a harbor tug is operated, its general operating characteristics such as the relative amount of time spent in each operation mode can be deduced. A wide variety of research in load prediction is available for marine vessels and land-based vehicles, which includes artificial neural networks, support vector machine, fuzzy network and numerical methods. While these methods are effective when a lot of data from measurement and system information is available, it is not straightforward to use information about general operating characteristics of a harbor tug to predict the load and demand and optimize using the formulation presented earlier. Therefore, a novel prediction scheme, which only requires information regarding the general characteristics of tugboat operation, is proposed in this research to forecast the load demand and then combined with the optimization formulation for power management presented earlier in this section to determine the engine and battery power outputs and the engine operation schedule.
The mechanism of prediction scheme is based on the historical load profile information and the general operational characteristics of a typical harbor tug profile shown in Figure 4, that is, the low-load demand occurs for around a = 65 percent of the overall operating cycle, the medium-load demand occurs for around b = 20 percent of the operating cycle and the high-load demand takes around c = 15 percent of the operating cycle. Let us say that in an operating time interval [0, nΔt]: anΔt, bnΔt, and cnΔt were the time intervals for which the tugboat operated in low-load, medium-load and high-load demand modes respectively. Here n, an, bn and cn are integers such that an + bn + cn = n and the sequences [an]n∞=1, [bn]n∞=1 and [cn]n∞=1 are increasing. To predict the load demand in the interval [nΔt, 2nΔt], the integers a2n, b2n and c2n are identified such that:
It can be verified that this prediction scheme ensure that the time percentages for which the tugboat operates in low load, medium load and high load satisfy:
The prediction scheme can be integrated with the power management optimization to successively find an optimal power management scheme over increasing time horizons. For the first iteration, when the load measurement is not available, the general operational characteristics can be used to determine the optimal schedule for engines and batteries. After the first iteration, the load profile during this interval is known and can be used to predict the load up to twice the length of the load measurement. Thus, the predicted load from a given time instant to the subsequent time horizon can be used for evaluating the optimal schedule for engines and batteries in the next iteration. This process can be repeated until the operation cycle terminates.