With the increasing population and standard of living, the energy demand is increasing. But the non-renewable resources are decreasing with the increasing demand for energy. So, we should focus on renewable energy resources. According to the report of REN21 in 2019, 73.8%of electricity is generated from non-renewable energy resources, and only 26.2%of electricity is generated from renewable energy resources where 15.8% is from Hydropower, 5.5% from Wind Power, 2.4% from solar PV (Photovoltaics), 2.2% from Bio-power and 0.4% from Geothermal, CSP and Ocean power. In this paper, we review the major renewable energy resources to determine effective and usable renewable resources. We especially focus on hydropower, wind power, and solar power, because those are the most used renewable resources at present. And finally, we develop a solar house system. To implement the solar house system, we used a solar array, a battery array, charge controller, inverter, and loads.

Gravitational waves are related to the concept of vibration of space-time curvature. When the body of heavy masses lies on the four-dimensional space-time and changes their position with turbulence motion then actually they create a disturbance in the space. The disturbance travels outward from the origin having light velocity is known as gravitational waves. Laser Interferometer Gravitational-Wave Observatory (LIGO) scientific teamwork declared the identification of these waves. In this paper, we review Gravitational waves, Detection of gravitational waves, deep learning for the classification of gravitational waves. We design and develop a deep learning system to classification gravitational waves of the dataset ‘Gravity Spy (Gravitational waves)’ that is made up of the LIGO images. The goals of this research are to gain a piece of reasonable and useful knowledge about Gravitational waves and propose an effective deep learning network system to classify the gravitational waves. The accuracy achieved by our model is 99.34%.