Disaggregation technology as a facilitator of desired behavioral change : Case of energy efficiency in Slovakia

A Recommendation System for Prosumers Based on Large Language Models

“…These attributes potentially impact the recommendations: (1) READING_DATE (Date and Hour) serves as the temporal marker for each data point, aligning recommendations with specific times and showing patterns over daily cycles; (2) OPEN_WEATHER (Weather forecast) directly impacts PV system output predictions and can influence decisions about power management based on anticipated solar generation capacity; (3) POWER_FORECAST (PV system output forecast) is a critical input for planning whether to store energy, sell surplus or manage deficits, impacting "Increase" and "Sell" decisions; (4) POWER_LOAD (Consumption Power) determines how much power is needed at any given time, influencing "Decrease" or "Increase" in load management; (5) POWER_GEN (Generated Power) is the actual power generation data influences real-time decisions on whether there is a surplus to sell or a need to draw from other sources; (6) POWER_BAT (Power extracted from Battery) indicates decisions on whether to draw power from the battery or to charge it depend on other power availability and demands; (7) POWER_GRID (Power extracted from Grid) indicates the usage of grid power indicates whether to buy additional power or manage with generated or stored power; (8) SD_CAPACITY (Battery capacity State of Charge) affects decisions on battery charging or discharging strategies; (9) LOAD_PERCENT (Percentage of the load from rated power of the PV system) indicates how heavily the system is loaded compared to its capacity, influencing load management strategies; (10) VPV (Voltage of the PV system) and ( 11) IPV (Current of the PV system) inform about the operational status and efficiency of the PV system, affecting decisions related to system load and generation management. Additional attributes, such as (12) Price_sell_to_grid also known as Feed-in-Tariff, (13) Price_buy_from_grid usually tariff rates that takes into account the consumption moment, (14) Price_sell_to_LEM and (15) Price_buy_from_LEM are economic factors and play a critical role, as the decision to buy or sell power (either to/from the grid or a LEM) is influenced by these prices.…”

“…There are several innovative technologies and platforms in the energy sector designed to support prosumers, such as (a) HEMS: Nest (Google Nest) well-known for its smart thermostats, which learn schedules and preferences to optimize heating and cooling for energy efficiency [11]; Tesla Energy offers solar panels, Solar Roof and Powerwall battery systems, integrating with a mobile app for energy monitoring and management [12]; (b) DR and energy optimization services: OhmConnect rewards users for saving energy during peak hours, integrating with smart home devices to automate energy savings; AutoGrid uses big data analytics and AI to offer DR, distributed energy resource management and energy storage optimization; (c) P2P energy trading platforms: LO3 Energy (Exergy) is a blockchain platform enabling LEM for P2P energy trading; Power Ledger utilizes blockchain technology to facilitate energy trading, allowing consumers to buy and sell renewable energy directly [13]; (d) Predictive analytics and AI for energy management: Bidgely utilizes AI and Machine Learning (ML) to disaggregate energy data from smart meters, providing personalized energy insights and recommendations [14]; Tibber is a digital electricity supplier that uses AI to optimize electricity consumption for its customers, offering dynamic pricing based on real-time market conditions; (e) Platforms integrating LLMs are a cutting-edge area of development and not yet sufficiently investigated. They have not been applied to prosumers' energy systems and thus we identified a research gap.…”

A Recommendation System for Prosumers Based on Large Language Models

“…There are several innovative technologies and platforms in the energy sector designed to support prosumers, such as (a) HEMSs like Nest (Google Nest), well-known for its smart thermostats which learn schedules and preferences to optimize heating and cooling for energy efficiency [ 19 ], and Tesla Energy, which offers solar panels, solar roof and battery systems, integrating with a mobile app for energy monitoring and management [ 20 ]; (b) DR and energy optimization services, like OhmConnect, which rewards users for saving energy during peak hours, integrating with smart home devices to automate energy savings, and AutoGrid, which uses big data analytics and AI to offer DR, distributed energy resource management and energy storage optimization; (c) P2P energy trading platforms, like LO3 Energy (Exergy), a blockchain platform enabling LEM for P2P energy trading, and Power Ledger, which utilizes blockchain technology to facilitate energy trading, allowing consumers to buy and sell renewable energy directly [ 21 ]; (d) predictive analytics and AI for energy management, like Bidgely, which utilizes AI and machine learning (ML) to disaggregate energy data from smart meters, providing personalized energy insights and recommendations [ 22 , 23 ], and Tibber, a digital electricity supplier that uses AI to optimize electricity consumption for its customers, offering dynamic pricing based on real-time market conditions; (e) platforms integrating LLMs, which are a cutting-edge area of development and not yet sufficiently investigated. They have not been applied to prosumers’ energy systems and thus we identified a research gap.…”