Agrivoltaic (agriculture–photovoltaic) or solar sharing has gained growing recognition as a promising means of integrating agriculture and solar-energy harvesting. Although this field offers great potential, data on the impact on crop growth and development are insufficient. As such, this study examines the impact of agriculture–photovoltaic farming on crops using energy information and communications technology (ICT). The researched crops were grapes, cultivated land was divided into six sections, photovoltaic panels were installed in three test areas, and not installed in the other three. A 1300 × 520 mm photovoltaic module was installed on a screen that was designed with a shading rate of 30%. In addition, to collect farming-cultivation-environment data and to analyze power generation, sensors for growing environments and wireless-communication devices were used. As a result, normal modules generated 25.2 MWh, bifacial modules generated 21.6 MWh, and transparent modules generated 25.7 MWh over a five-month period. We could not find a difference in grape growth according to the difference of each module. However, a slight slowing of grape growth was found in the experiment group compared to the control group. Nevertheless, the sugar content of the test area of the grape fruit in the harvest season was 17.6 Brix on average, and the sugar content of the control area was measured at 17.2 Brix. Grape sugar-content level was shown to be at almost the same level as that in the control group by delaying the harvest time by about 10 days. In conclusion, this study shows that it is possible to produce renewable energy without any meaningful negative impact on normal grape farming.

Building energy management systems (BEMS) have been intensively used to manage the electricity consumption of residential buildings more efficiently. However, the dynamic behavior of the occupants introduces uncertainty problems that affect the performance of the BEMS. To address this uncertainty problem, the BEMS may implement load forecasting as one of the BEMS modules. Load forecasting utilizes historical load data to compute model predictions for a specific time in the future. Recently, smart meters have been introduced to collect electricity consumption data. Smart meters not only capture aggregation data, but also individual data that is more frequently close to real-time. The processing of both smart meter data types for load forecasting can enhance the performance of the BEMS when confronted with uncertainty problems. The collection of smart meter data can be processed using a batch approach for short-term load forecasting, while the real-time smart meter data can be processed for very short-term load forecasting, which adjusts the short-term load forecasting to adapt to the dynamic behavior of the occupants. This approach requires different data processing techniques for aggregation and individual of smart meter data. In this paper, we propose Lambda-based data processing architecture to process the different types of smart meter data and implement the two-level load forecasting approach, which combines short-term and very short-term load forecasting techniques on top of our proposed data processing architecture. The proposed approach is expected to enhance the BEMS to address the uncertainty problem in order to process data in less time. Our experiment showed that the proposed approaches improved the accuracy by 7% compared to a typical BEMS with only one load forecasting technique, and had the lowest computation time when processing the smart meter data.