Review on optimization techniques and role of Artificial Intelligence in home energy management systems

Nutakki, Mounica; Mandava, Srihari

doi:10.1016/j.engappai.2022.105721

Cited by 50 publications

(13 citation statements)

References 57 publications

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“…(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.…”

Section: Inputmentioning

confidence: 99%

A Recommendation System for Prosumers Based on Large Language Models

Oprea,

Bâra

2024

Sensors

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As modern technologies, particularly home assistant devices and sensors, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information at 15-min or even 5-min intervals, including weather forecasts, outputs from renewable energy source (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the local energy market (LEM). The goal for these prosumers is to reduce costs while ensuring their home’s comfort levels are maintained. However, given the complexity and the rapid decision-making required in managing this information, the need for a supportive system is evident. This is particularly true given the routine nature of these decisions, highlighting the potential for a system that provides personalized recommendations to optimize energy consumption, whether that involves adjusting the load or engaging in transactions with the LEM. In this context, we propose a recommendation system powered by large language models (LLMs), Scikit-llm and zero-shot classifiers, designed to evaluate specific scenarios and offer tailored advice for prosumers based on the available data at any given moment. Two scenarios for a prosumer of 5.9 kW are assessed using candidate labels, such as Decrease, Increase, Sell and Buy. A comparison with a content-based filtering system is provided considering the performance metrics that are relevant for prosumers.

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Section: Inputmentioning

confidence: 99%

A Recommendation System for Prosumers Based on Large Language Models

Oprea,

Bâra

2024

Sensors

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“…Artificial neural network (ANN), ensemble or hybrid models, decision tree regression, random forest, regression with support vectors, and extreme learning machines (ELMs) are also common in any time series study. For the period of 2016–2017, the author of [ 9 ] examined four ML approaches for forecasting short‐term and long‐term power consumption in Cyprus.…”

Section: Introductionmentioning

confidence: 99%

“…Utilizing population, economy, and climate data, the model forecasted future power requirements, with support vector machine (SVM) and ANN techniques demonstrating superior performance compared to multiple linear regression. [ 9 ] In, [ 10 ] a more advanced ML framework was proposed by combining SVM and ELM. The Grid search technique was employed to determine optimal hyper parameter values in the study.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Modeling and Optimization of Solar Plant Production Integration in the Smart Grid Using Machine Learning Models

Abubakar,

Che,

Faheem

et al. 2024

Adv Energy and Sustain Res

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To address the rising energy demands in industrial and public sectors, integrating zero‐carbon emission energy sources into the power grid is crucial. Smart grids, equipped with advanced sensing, computing, and communication technologies, offer an efficient way to incorporate renewable energy resources and manage power systems effectively. However, improving solar energy efficiency, which currently contributes around 3.6% to global electricity, is a challenge in smart grid infrastructures. This research tackles this issue by deploying machine learning models, specifically recurrent neural network (RNN), long short‐term memory (LSTM), and gate recurrent unit (GRU), to predict measurements that could enhance solar power generation in smart grids. The objective is to boost both performance and accuracy of solar power generation in the smart grid. The study conducts experimental analyses and performance evaluations of these models in smart grid environments, considering factors like power output, irradiance, and performance ratio. The results, presented through graphical visualizations, show notable improvements, particularly with the LSTM model, which achieves a 97% accuracy, outperforming the RNN and GRU models. This outcome highlights the LSTM model's effectiveness in accurately predicting measurements, thereby advancing solar power generation efficiency in the smart grid framework.

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“…General types of recommendation systems and technologies that play a significant role for prosumers in the energy sector are usually associated with: (a) Smart Home Energy Management Systems (HEMS). These systems integrate with smart home devices and RES (like solar panels) to optimize energy consumption [2]. They may suggest the best times to use energy-intensive appliances based on the lowest energy prices or highest RES production, contributing to cost savings and increased energy efficiency [3,4]; (b) Demand Response (DR) programs.…”

Section: Introductionmentioning

confidence: 99%

A Recommendation System for Prosumers Based on Large Language Models

Oprea,

Bâra

2024

Preprint

View full text Add to dashboard Cite

As modern technologies, particularly home assistant devices, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information in 15-minute or even 5-minute intervals, including weather forecasts, outputs from Renewable Energy Sources (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the Local Energy Market (LEM). The goal for these prosumers is to reduce costs while ensuring their home’s comfort levels are maintained. However, given the complexity and the rapid decision-making required in managing this information, the need for a supportive system is evident. This is particularly true given the routine nature of these decisions, highlighting the potential for a system that provides personalized recommendations to optimize energy consumption, whether that involves adjusting the load or engaging in transactions with the LEM. In this context, we propose a recommendation system powered by Large Language Models (LLM), Scikit-llm and Zero-Shot Classifiers, designed to evaluate specific scenarios and offer tailored advice for prosumers based on the available data at any given moment. Two scenarios for a prosumer of 5.9 kW are assessed using candidate labels, such as Decrease, Increase, Sell and Buy.

show abstract

Review on optimization techniques and role of Artificial Intelligence in home energy management systems

Cited by 50 publications

References 57 publications

A Recommendation System for Prosumers Based on Large Language Models

A Recommendation System for Prosumers Based on Large Language Models

Intelligent Modeling and Optimization of Solar Plant Production Integration in the Smart Grid Using Machine Learning Models

A Recommendation System for Prosumers Based on Large Language Models

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