Electricity Demand Forecasting
Planning energy use requires accurate forecasts of electricity demand. To illustrate the benefits
of sophisticated machine learning techniques over conventional methods, Bedi and Toshniwal
(2019) presented a deep learning framework specifically intended for electricity demand
forecasting. The significance of incorporating intricate data patterns to enhance forecasting
precision is underscored by their research, which holds special advantages for areas such as
Palawan, where demand is subject to variations owing to regional economic activities and
tourism.
In the same way, Jiang et al. (2020) used data processing and improved support vector machines
to create a composite framework for forecasting power consumption. It would be possible to
modify this strategy for Palawan's particular energy environment, where demand patterns could
be greatly impacted by regional elements like seasonal travel.
Globally, the COVID-19 epidemic has altered patterns of electricity usage and brought up
unprecedented challenges. Economic downturns can lead to reduced energy consumption, as the
study conducted by Norouzi et al. (2020) on the impact of COVID-19 on China's oil and power
demand demonstrates. This result is supported by Abu-Rayash and Dincer (2020), who looked at
trends in electricity demand during the pandemic and concluded that significant changes in
consumption patterns necessitate the use of flexible energy management strategies.
According to Santiago et al. (2020), who conducted more study on the pandemic's effects on
Spain's electricity use, lockdown measures dramatically reduced the nation's electrical use. These
results suggest that similar studies could be necessary for Palawan to understand the ways in
which external
Renewable Energy Integration and Demand Management
Sustainability depends on the addition of renewable energy sources to the mix of electricity
sources. Azizi et al. (2014) evaluated whether property would be suitable for wind power
facilities, emphasizing the role that environmental and geographic considerations have in the
development of renewable energy. Undertaking a comparable evaluation in Palawan would
pinpoint appropriate sites for the installation of renewable energy systems, so augmenting the
availability of electricity and possibly stabilizing the market.
In order to provide sustainable power for smart grids, Jamil et al. (2021) presented a peer-to-peer
energy trading system that makes use of blockchain technology and machine learning. With
limited grid access in rural regions, this creative solution has the potential to optimize electricity
use and empower communities in Palawan through local energy trade.
Knowledge Gaps and Future Research Directions
Although previous research has provided valuable insights, there are still a number of
unanswered questions concerning Palawan's electricity consumption. For example, there aren't
many studies that are especially focused on the distinct socio-economic and environmental
circumstances found in Palawan. Future studies might concentrate on:
1. **Localized Demand Forecasting**: Creating customized models of demand that take into
account seasonal variations, tourism trends, and socioeconomic factors specific to the area.
2. **Impact of Renewable Energy**: Assessing if Palawan-specific renewable energy sources,
like biomass, solar, and wind, can meet the island's electrical requirements.
3. **Survivability in the Face of External Shocks**: investigating how catastrophes like as
pandemics or natural disasters affect Palawan's power supply and demand and creating backup
plans.
4. **Community Energy Solutions**: Investigating neighborhood-based energy ideas to improve
sustainability and accessibility, such as microgrid construction and peer-to-peer trading.
A wealth of knowledge is available to address the power demand issues facing the Province of
Palawan from the literature on electricity demand forecasting, external impacts, and renewable
energy integration. Palawan can create an energy framework that is more resilient and
sustainable by utilizing cutting-edge forecasting approaches, comprehending external shocks,
and encouraging the use of renewable energy sources. It will be imperative that future research
endeavors concentrate on localized studies in order to inform successful energy policies and
strategies in this particular location.
