To maintain grid stability, the energy levels produced by sources within the network must be equal to the energy consumed by customers. In current times, achieving energy balance mainly involves regulating the electrical energy sources, as consumption is typically beyond the control of grid operators. For improving the stability of the grid, accurate forecasting of photovoltaic power output from largely integrated solar photovoltaic plant connected to grid is required. In the present study, to improve the forecasting accuracy of the forecasting models, onsite measurements of the weather parameters and the photovoltaic power output from the 20 kW on‐grid were collected for a typical year which covers all four seasons and evaluated the random forest techniques and other techniques like deep neural networks, artificial neural networks and support vector regression (reference in this study). The simulation results show that the proposed random forest technique for the forecasting horizon of 15 and 30 min is performing well with 49% and 50% improvements in the accuracy respectively over reference model for the study location 22.78°N, 73.65°E, College of Agricultural Engineering and Technology, Anand Agricultural University, Godhra, India.

To meet the creeping electricity demand, the best option is to tap the energy from the Renewable energy sources and from solar in particular for filling the gap between supply and demand. This paper is aimed at the design of an off-grid photovoltaic (PV) systems which is able to fulfil the electrical power demand in the standalone condition. Various components like solar PV panel, inverter, charge controller, batteries are parts of the system design. The estimated load is 31.30 kWh/ day. 8 kW PV array capacity, 46 PV modules, 16 batteries (12V, 200Ah), a voltage regulators (45A, 24V) and an inverter (10kVA, 48V) were used under the study. The design proposed requires wires made of copper with area of cross-section 1.5 mm ² , 22.0 mm ² and 2.5mm ² for installation of panel or charge controller, battery or inverter and load connected wires. The estimated cost of the system worked out to be about Rs. 14.0 lakhs. The systems payback period is estimated to be 13.19 years. The insights from the results of the design can be extended to the other solar PV projects of the same kind.

Forecasting solar photovoltaic power ensures a stable and dependable power grid. Given its dependence on stochastic weather conditions, predicting solar photovoltaic power accurately demands applying intelligent and sophisticated techniques capable of handling its inherent nonlinearity and volatility. Controlling electrical energy sources is an important strategy for reaching this energy balance because grid operators often have no control over use patterns. Accurately forecasting photovoltaic (PV) power generation from highly integrated solar plants to the grid is essential for grid stability. This study aims to improve forecasting accuracy and make accurate predictions of solar power output from the selected grid-connected PV system. In this study, the weather data was collected on-site and recorded PV power from a 20 kW on-grid system for one year, and different machine learning techniques like deep neural networks, random forests, and artificial neural networks were evaluated and benchmarked against reference support vector regression model. With improvements in forecasting accuracy of 2 to 37% over the reference model at study location (22.780 N, 73.650 E), College of Agricultural Engineering and Technology, Anand Agricultural University, Godhra, India, simulation results showed that the random forest technique is effective for the forecasting horizons of 1 to 4 hours.

A Multi-Purpose Solar Food Processor (MCSFP) was developed to carryout both cooking and drying operations simultaneously. MCSFP was tested in three different modes of operations ie., for fenugreek leaves drying (M1) and for cooking of rice separately (M2) and drying of fenugreek and cooking of rice together(M3). The maximum temperature difference attained between ambient air and MCSFP dryer and cooker was 23°C and 36.5°C respectively. The temperatures attained are well suited for drying of fenugreek leaves and cooking rice.

Fluctuating solar radiation greatly diminishes the output power from the solar photovoltaic modules. The most capable approach for improving the power output is considered as reconfiguration of solar photovoltaic modules through dynamically switching electrical connections. Efforts are made in this paper to discuss the current state of photovoltaic PV systems, sizing, optimizing and reconfiguration methodologies and recommended the efficient reconfiguration technique.