Demand analysis of solar power generation

How can we increase demand for solar and wind energy?

Increasing the share of demand that can be met by solar and wind generation will require either “overbuilding” (i.e., excess annual generation), the introduction of large-scale energy storage, and/or aggregating resources across multinational regions (Supplementary Data 6).

How can solar and wind power meet global electricity demand?

With solar and wind capacities sized such that total annual generation meets total annual demand, seasonal and daily complementarities of these resources make them capable of meeting three-quarters of hourly electricity demand in larger countries.

How is PV power generation forecasting based on climatic data?

PV power generation forecasting is long-term by considering climatic data such as solar irradiance, temperature and humidity. Moreover, we implemented these deep learning methods on two datasets, the first one is made of electrical consumption data collected from smart meters installed at consumers in Douala.

How to estimate solar energy potential from alternative technologies?

The average value of the solar radiation is 3.3 while the predicted value is 3.7 in February and thus we may distinguish the changes in solar radiation between different months. To estimate solar energy potential from alternative technologies, we have to multiply the sunny hours with the solar energy conversion rate.

What is the contribution of solar energy to global electricity production?

While the contribution of solar energy to global electricity production remains generally low at 3.6%, it has firmly established itself among other renewable energy technologies, comprising nearly 31% of the total installed renewable energy capacity in 2022 (IRENA, 2023).

Is a hybrid model good for solar PV power generation forecasting?

Table 8. Comparison with the literature on PV power generation forecasting. that the proposed hybrid model is better than those in the literature with minimum error and highest regression. 4. Conclusion This study aims to present deep learning algorithms for electrical demand prediction and solar PV power generation forecasting.