In the vast, untapped energy landscape of Indonesia, a beacon of innovation is emerging from the waves surrounding Bawean Island. Risma Madurahma Putri, a researcher from the Mathematics Department at UIN Sunan Ampel Surabaya, has been delving into the potential of wave energy, specifically using Oscillating Water Column (OWC) technology. Her work, published in the journal ‘Barekeng’ (translated as ‘Light’ or ‘Illumination’), offers a promising solution to the island’s long-standing electricity woes and presents a compelling opportunity for the maritime sector.
Putri’s research focuses on predicting the electric power generated by OWC wave power plants. By analyzing time series data from January 1, 2021, to May 5, 2024, she’s tackled the challenge of forecasting wave parameters like height, length, period, and amplitude. These parameters are crucial for estimating the power output of wave energy converters. The tool of her trade? A deep learning method called Long Short Term Memory (LSTM), which is particularly adept at handling sequential data like time series.
So, what does this mean for Bawean Island and the maritime sector at large? Well, imagine a future where the ebb and flow of the ocean tides aren’t just a natural phenomenon, but a reliable source of electricity. Putri’s research brings us a step closer to that reality. By accurately predicting the power output of OWC plants, she’s helping to ensure a stable electricity supply, mitigating the risk of mismatches between supply and demand.
The commercial implications are substantial. Wave energy is a renewable, sustainable resource, and its potential is immense. According to the International Energy Agency, wave energy could potentially power 15% of the world’s electricity demand. For maritime professionals, this presents an opportunity to diversify energy portfolios, reduce carbon footprints, and tap into a new revenue stream.
Moreover, the integration of AI and machine learning methods like LSTM into wave energy prediction models is a game-changer. It enables more accurate forecasting, better resource management, and ultimately, more efficient power generation. As Putri puts it, “The best prediction results for the variables of height, length, period, and amplitude of the waves obtained MAPE (Mean Absolute Percentage Error) values of 0.3657%, 0.1637%, 0.0888%, and 0.3480%, respectively. The electricity prediction results from the best parameters obtained a MAPE of 0.3549%.” In layman’s terms, this means the model is highly accurate, paving the way for reliable wave energy predictions.
For Bawean Island, this research could be a lifeline, addressing the island’s electricity supply limitations and promoting more equitable electricity distribution among its communities. For the maritime sector, it’s a call to action, an invitation to explore the vast potential of wave energy and harness the power of AI to drive innovation and sustainability.
As we navigate the complexities of the 21st century, one thing is clear: the future of energy lies not in depletion, but in renewal. And in the waves around Bawean Island, that future is already beginning to take shape.