AI-driven energy efficiency recommendations

Generative AI models like LLMs (Large Language Models) can significantly contribute to energy efficiency recommendations. For example, these AI models can evaluate extensive energy usage datasets from various sources, interpret patterns, and subsequently devise energy-efficient strategies. An AI model can analyze the power consumption data from a manufacturing unit and suggest tweaks in operation times or pattern to reduce energy waste.

Similarly, image generators can be utilized in rendering precise 3D models of buildings or cities. These models can be analyzed to identify energy leakage hotspots and provide recommendations for insulation or design improvements. AI could also be applied to predict solar panel efficiency based on climate, location, and time data, further aiding in the planning and optimization of alternative energy sources.

With continuous learning and adaption, these AI models can provide dynamic, intelligent, and location-specific energy efficiency recommendations.

Using Clevis, you can build a wide array of applications, including an AI-driven energy saving recommendations app as follows. After you input the initial configuration, it performs several steps in sequence. First, Text Input requests the user to provide their location. The application uses this information to fetch local energy data via the Http Request function.

Next, the app makes use of a specific task of OpenAI, ChatGPT. It prompts ChatGPT with this fetched energy data, leveraging its functionality to craft personalized energy-saving tips. Essentially, the role of ChatGPT in this application is to analyze location-based energy data, draw relevant insights, and turn those into practical recommendations for energy efficiency.

Once these steps have been carried out, the app finally utilizes the Display Output function. It curates the personally suited energy tips generated by ChatGPT and presents them to the user in a user-friendly display. This way, users get personalized, actionable insights to better manage their energy consumption, driving towards efficiency. This is just an example of what you can accomplish using Clevis, as the platform is versatile enough to build many other AI applications within the same area.