ANN–GA smart appliance scheduling for optimised energy management in the domestic sector

Yüce, Barış; Rezgui, Yacine; Mourshed, Monjur

doi:10.1016/j.enbuild.2015.11.017

Cited by 125 publications

(63 citation statements)

References 20 publications

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“…ANN mimics the biological neural system to find correlations for complex systems without having an explicit functional relationship [11]. These relations are defined with artificial neurons and their artificial importance (weight) with transfer functions.…”

Section: Artificial Neural Network For District Energy Managementmentioning

confidence: 99%

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A Smart Forecasting Approach to District Energy Management

2017

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Abstract:This study presents a model for district-level electricity demand forecasting using a set of Artificial Neural Networks (ANNs) (parallel ANNs) based on current energy loads and social parameters such as occupancy. A comprehensive sensitivity analysis is conducted to select the inputs of the ANN by considering external weather conditions, occupancy type, main income providers' employment status and related variables for the fuel poverty index. Moreover, a detailed parameter tuning is conducted using various configurations for each individual ANN. The study also demonstrates the strength of the parallel ANN models in different seasons of the years. In the proposed district level energy forecasting model, the training and testing stages of parallel ANNs utilise dataset of a group of six buildings. The aim of each individual ANN is to predict electricity consumption and the aggregated demand in sub-hourly time-steps. The inputs of each ANN are determined using Principal Component Analysis (PCA) and Multiple Regression Analysis (MRA) methods. The accuracy and consistency of ANN predictions are evaluated using Pearson coefficient and average percentage error, and against four seasons: winter, spring, summer, and autumn. The lowest prediction error for the aggregated demand is about 4.51% for winter season and the largest prediction error is found as 8.82% for spring season. The results demonstrate that peak demand can be predicted successfully, and utilised to forecast and provide demand-side flexibility to the aggregators for effective management of district energy systems.

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Section: Artificial Neural Network For District Energy Managementmentioning

confidence: 99%

“…ANNs involve high performance, fast and non-linear analytics. The study presented in [11] utilises ANN as a cost function engine for the optimisation system. One of the key elements to highlight about the ANN is that each developed ANN is problem specific.…”

Section: Artificial Neural Network For District Energy Managementmentioning

confidence: 99%

A Smart Forecasting Approach to District Energy Management

2017

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“…Computational time is not practical GWO Economic dispatch using the hybrid gray wolf optimizer [31] Solving non-linear economic load dispatch problems…”

Section: Mimomentioning

confidence: 99%

Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

et al. 2018

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With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, demand side management (DSM) is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE), is proposed by merging enhanced differential evolution (EDE) and gray wolf optimization (GWO) scheme using real-time pricing (RTP) and critical peak pricing (CPP). Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR) is reduced to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30-and 60-min operational time interval (OTI). On the other hand, the PAR and electricity bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff.

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“…In 88 [19], a residential energy consumption scheduling of electrical 89 and thermal appliances to minimize energy costs of a customer 90 with a RES is proposed taking its comfort into consideration. 91 An artificial intelligence based smart appliance scheduling ap-92 proach for reducing energy demand in peak periods by maxi-93 mizing the use of RES in the residential sector is proposed in 94 [20]. Other EMS that consider the ownership of both an on-95 site RES and an ESS in each household have been considered 96 in [21][22][23][24].…”

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confidence: 99%

“…Hence, the management of 16 households' appliances power consumption can play an impor-17 tant role in saving costs and reducing the environmental impact 18 of the electricity consumed in the residential sector. 19 Accordingly, Demand Response (DR) programs have been 20 defined, providing several economic and technical benefits for 21 utilities and consumers [4]. Namely, DR programs aim to re-22 shape consumer energy profiles in order to improve the relia-23 bility and efficiency of the grid and defer generation capacity 24 expansion [5,6].…”

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confidence: 99%

Reputation-based joint scheduling of households appliances and storage in a microgrid with a shared battery

AlSkaif

Luna

Zapata

et al. 2017

Energy and Buildings

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Due to the decreasing revenues from the surplus renewable energy injected into the grid, mechanisms promoting self-consumption of this energy are becoming increasingly important. Demand Response (DR) and local storage are among the widely used mechanisms for reaching higher self-consumption levels. Deploying a shared storage unit in a residential microgrid is an alternative scenario that allows households to store their surplus renewable energy for a later use. However, this creates some challenges in managing the battery and the available energy resource in a fair way. In this paper, a reputation-based centralized Energy Management System (EMS) is proposed to deal with these issues by considering households' reputations in the reallocation of available energy in the shared storage unit. This framework is used in an optimization problem, in which the EMS jointly schedules households' appliances power consumption and the energy that each household can receive from the storage unit. The scheduling problem is formulated as a Mixed Integer Linear Programming (MILP) with the objective of minimizing the amount and price of energy absorbed from the main grid. The MILP problem is coded in GAMS and solved using CPLEX. Numerical analysis is conducted using real data of renewable energy production and appliances' demand profiles for different classes of households and different annual periods in Spain. Simulation results of the different scenarios show that by using the proposed framework higher cost savings can be achieved, in comparison with the classical scheduling scenario. The saving can reach up to 68% when different classes of households exist in the microgrid. The results also show that the fairness in energy allocation is guaranteed by the reputation-based policy, and that the total power absorbed from the main grid by the whole microgrid is significantly decreased.

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ANN–GA smart appliance scheduling for optimised energy management in the domestic sector

Cited by 125 publications

References 20 publications

A Smart Forecasting Approach to District Energy Management

A Smart Forecasting Approach to District Energy Management

Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

Reputation-based joint scheduling of households appliances and storage in a microgrid with a shared battery

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