GA strategies for optimal planning of daily energy consumptions and user satisfaction in buildings

Pallotti, Emiliano; Mangiatordi, Federica; Fasano, Matteo; Vecchio, Paolo del

doi:10.1109/eeeic.2013.6549556

Cited by 12 publications

(5 citation statements)

References 15 publications

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“…DSM can be described as variation in the standard consumption patterns of electricity by end users according to variation in the electricity cost through time [6,7]. DSM focuses on utilizing power-saving technologies, electricity tariffs, monetary incentives, and government policies [8]. The main objective of DSM is to reduce the system peak demand and operational costs.…”

Section: Demand-side Management (Dsm) Impactmentioning

confidence: 99%

Demand-Side Management and Its Impact on the Growing Circular Debt of Pakistan’s Energy Sector

Hassan,

Khan,

Zia

et al. 2023

Energies

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In this research, we propose an energy-management scheme for domestic users, which uses the load-shifting strategy of demand-side management (DSM). The research demonstrates that the energy sector’s circular debt problem from the viewpoint of a developing country can be solved by incorporating DSM. Circular debt is a chain reaction that arises when the balance between cost and energy supply collapses. Circular debt is an ongoing problem in Pakistan, where economic crises are continuously posing a threat to the energy sector. DSM is envisioned to address these concerns in a dynamic way thoroughly: introducing DSM can minimize circular debt, increase grid reliability, and smooth the supply–demand operation. Circular debt is directly linked with the subsidy offered by the government of Pakistan. As the cost of energy utilized by consumers increases, the subsidy also increases due to the direct link between the two entities. Therefore, the subsidy can be controlled by energy-consumption management with the adoption of DSM. This study addresses that by incorporating optimized cost solutions, circular debt can be regulated to improve the economy of the energy sector. A genetic algorithm is used as an optimization tool to manage demand and generate an optimal schedule under a dynamic electricity pricing signal. To support the utility, a solar system is used as a secondary energy source. Finally, the results show a curtailment in the payable costs at both the consumer and government ends, thus reducing the circular debt in the bigger picture. The reduction is 18% without and 41% with renewable energy support.

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Section: Demand-side Management (Dsm) Impactmentioning

confidence: 99%

Demand-Side Management and Its Impact on the Growing Circular Debt of Pakistan’s Energy Sector

Hassan,

Khan,

Zia

et al. 2023

Energies

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“…A system that produced a real-time solution to reduce the electricity cost and to avoid the high peak demand problem for a smart home was proposed by Özkan (2015). The use of an heuristic strategy to find the optimal planning of energy consumption inside every building in a neighbourhood is investigated in Pallotti et al (2013). The issue is formulated as multi-objective optimisation problem aiming at reducing the peak load as well as minimising the energy cost and the impact on the user satisfaction.…”

Section: Related Workmentioning

confidence: 99%

A genetic algorithm for real-time demand side management in smart-microgrids

Venticinque¹,

Diodati²

2022

IJCSE

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One of the main drawbacks in the management of renewable resources, including wind and solar energies, is the issue related to the uncertainty in their behaviour. Demand side management (DSM) shifts loads of a household from times characterised by a surplus in consumption to times with photovoltaic production surplus. In this paper we propose the utilisation of a genetic algorithm to find the schedule of energy loads that best matches the energy produced by photovoltaic panels. We aim at optimising self-consumption, but satisfying real-time constraints, which allow for addressing unforeseen changes of the planned schedule or unpredictable variations of renewable energy production. We designed specialised genetic operators to accelerate, already in the first iterations, the convergence to a local minima of the solution space, and evaluated how such improvements affect the optimality of results.

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“…When considering optimization approaches, various applicable methodologies can be found in the related literature. Some authors employ complex nature-inspired heuristics such as the genetic algorithm as in [26,27], particle swarm optimizations as in [28], as well as artificial neural networks like in article [29]. However, when working with data with medium-sized resolutions such is the case with hourly measurements that are most often given by RES production and demand forecasting algorithms, more efficient algorithms with simplified models such as linear programming and its extension, mixed-integer linear programming (MILP), are used more often.…”

Section: Related Workmentioning

confidence: 99%

An AI-Powered System for Residential Demand Response

et al. 2021

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Recent studies show that energy consumption of buildings has dramatically increased over the last decade, accounting for more than 35% of global energy use. However, with proper operation, significant energy savings can be achieved. Demand response is envisioned as a key enabler of this operation enhancement, as it may contribute to the reduction of demand peaks and maximization of renewable energy exploitation while mitigating potential problems with grid stability. In this article, a system based on artificial intelligence that solves the complex multi-objective problem to bring demand response programs to the residential sector is proposed. Through the application of novel machine learning-based algorithms, a unique control loop is developed to help dwellers determine how and when to use their appliances. The feasibility and validity of the proposed system has been demonstrated in a real-world neighbourhood where a notable reduction and shift of electricity demand peaks has been achieved. Concretely, in accordance with extreme changes in the energy prices, the users have demonstrated the ability to shift their demand to periods with lower prices as well as reducing power consumption during periods with higher prices, thus fully translating the demand peak in time.

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GA strategies for optimal planning of daily energy consumptions and user satisfaction in buildings

Cited by 12 publications

References 15 publications

Demand-Side Management and Its Impact on the Growing Circular Debt of Pakistan’s Energy Sector

Demand-Side Management and Its Impact on the Growing Circular Debt of Pakistan’s Energy Sector

A genetic algorithm for real-time demand side management in smart-microgrids

An AI-Powered System for Residential Demand Response

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