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In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. The proposed DNN utilises a number of different methodologies, two of which are cloud motion analysis and machine learning, in order to make forecasts regarding the climatological conditions of the future. In addition to this, the accuracy of the model was evaluated in light of the data sources that were easily accessible. In general, four different cases have been investigated. According to the findings, the DNN is capable of making more accurate and reliable predictions of the incoming solar irradiance than the persistent algorithm. This is the case across the board. Even without any actual data, the proposed model is considered to be state-of-the-art because it outperforms the current NWP forecasts for the same time horizon as those forecasts. When making predictions for the short term, using actual data to reduce the margin of error can be helpful. When making predictions for the long term, however, weather information can be beneficial.

In this chapter, the precision agricultural and waste management systems using artificial intelligent techniques are described. The fourth agricultural revolution integrates cross-industry technology to increase precision agriculture output and efficiency. Predictive analytics can be used to incorporate massive amounts of data generated by wireless smart networks and the internet of things. “Smart farming” aims to improve both the quantity and quality of agricultural products. The internet of things (IoT) is being used to improve agro-waste management, as well as crop classification and disease detection. Garbage collection and wireless sensor networks on smart bins are employed in the IOT to improve waste management. Vermicomposting is a process that uses earthworms and other associated bacteria to create incredibly fertile compost. Waste management methods for flower waste, bagasse, banana agro-waste, and agro-industrial wastewater have been depicted. Smart waste management for precision agriculture systems is also illustrated.

Currently the industrial heat demand is met by using expensive fossil fuels. Exclusive use of solar energy is not feasible due to the fluctuating pattern of solar radiation intensity. Solar hybridization with the existing heating system can be an appropriate solution to meet the process heat requirement of many industries. Concentrator Solar Thermal (CST) technologies can generate the medium temperature heat required for industrial processes. The present study was undertaken with an objective of comparing and analyzing the designed performance of the solar fields using the Compound Parabolic Concentrator (CPC) technology against the actual measured performance values for boiler feed water preheating application at two different locations in India. The optical efficiency of the CPC collector, 64.8%, obtained when tested as per part 5 of IS 16648:2017 was used for designing the solar fields as per the daily heat requirement. The performance of the installations at both the locations was monitored for a period of five months. The observed variation in the performance of each installation than the designed performance was compared and analyzed for the causes. The average variation in designed and measured performance was in the range of 9.0% to 9.8% for location 1 and 2 respectively, attributing to heat rejection from the collector attachments and fluid transfer lines, dust effect on the absorber and reflector of CPC, instrument’s uncertainty, other losses due to shadow effect, vacuum loss from the tubes, dislocation of tubes, heat removal and usage pattern etc. The reasons of the losses from both the fields were of the similar nature, which should be taken into account to design a solar thermal system to achieve predicted performance near to the designed performance. Preheating of boiler feed water is one of the potential applications of solar CPC technology.