# Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy

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## Abstract

**:**

## 1. Introduction

## 2. Load Model of Home Appliances

#### 2.1. Air Conditioner Modeling

^{2}), respectively; ${S}_{\mathrm{HGC}}$ is the solar heat gain coefficient of a window [26]; $(\text{}{R}_{\mathrm{fl}},\text{}{R}_{\mathrm{wal}}$, ${R}_{\mathrm{ce}},\text{}{A}_{\mathrm{win}})\text{}$are the average thermal resistance of the floor, wall, ceiling, and window in ($\mathbb{C}\xb7$m

^{2}$\xb7$h/$\mathrm{btu}$), respectively; ${T}_{\mathrm{out},t}\text{}$is the outside temperature ($\mathbb{C}$) [27]; ${A}_{\mathrm{win}\_\mathrm{s}}$ is the window area facing south (m

^{2}); $S$ is the air heat factor ($\mathrm{btu}$/$\mathbb{C}\xb7{\mathrm{m}}^{3}$); and ${H}_{\mathrm{solar}}$ is the solar radiation heat power (W/m

^{2}).

_{p}, is 0.2099$/{\mathrm{m}}^{2}\xb7\text{}\mathbb{C}$, and the house volume, ${V}_{\mathrm{hos}}$, in ${\mathrm{m}}^{3}$, is included in Equation (3):

#### 2.2. Electric Water Heater Modeling

^{3}/s), $Vo{l}_{\mathrm{tank}}$ is the volume of the tank (m

^{3}), ${A}_{\mathrm{tank}}$ is the surface area of the tank, ${T}_{\mathrm{amp}}$ is the ambient temperature, ${R}_{\mathrm{tank}}$ is the heat resistance of the tank ($\mathbb{C}\xb7$m

^{3}$\xb7$h/$\mathrm{btu}$), and $\mathrm{dt}$ is the duration of the time slot t.

#### 2.3. Water Heater and Refrigerator Modeling

## 3. Gathering Data for Household Appliance Models

## 4. Artificial Intelligent Techniques Used for Home Energy Management Scheduling Controller

#### 4.1. Artificial Neural Network Technique

#### 4.2. Overview of Lightning Search Algorithm

#### 4.3. Proposed Hybrid Lightning Search Algorithm-Based Artificial Neural Network

## 5. Overall Proposed Home Energy Management Scheduling Controller System

## 6. Results and Discussion

#### 6.1. Home Appliance Simulation Result

#### 6.1.1. Water Heater Simulation Result

#### 6.1.2. Air Conditioner Simulation Results

#### 6.2. Experimental Measurement Data

#### 6.3. Results of the Hybrid Lightning Search Algorithm-Based Artificial Neural Network

^{−9}after 40 iterations. The population size of 50 needs more working time than the population size of 40. The ANN parameters based on the results of the hybrid LSA-ANN are shown in Table 1. The PSO algorithm is also implemented to obtain the same objective for 10, 20, 30, 40 and 50 population sizes for comparison with the results from the hybrid LSA-ANN, as shown in Figure 23.

^{−9}after 40 iterations at a population size of 40. The hybrid PSO-ANN obtains a MAE error of 1.195 × 10

^{−8}after 81 iterations at a population size of 40, as illustrated in Figure 24.

#### 6.4. Results of the Proposed Hybrid LSA-ANN Based Home Energy Management Scheduling Controller

## 7. Conclusions

^{−9}, whereas the hybrid PSO-ANN achieves a MAE error of 1.195 × 10

^{−8}. The proposed algorithm shows a better response in switching the status in HEMSC. Therefore, the energy saving for the total power by using the hybrid LSA-ANN is 9.7138% per 7 h, whereas that by using the hybrid PSO-ANN is 2.3817% per 7 h. The results explain the capability of the proposed HEMSC algorithm to maintain the total electrical energy consumption below the DL value during a DR event. Moreover, the algorithm easily deals with the DR signals and is more effective in energy saving.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**Figure 1.**Block diagram of the proposed home energy management scheduling controller (HEMSC) system. AC: air conditioner; REF: refrigerator; WM: washing machine; WH: water heater.

**Figure 16.**Simulation model of EWH load: (

**a**) flow rate of the hot water in gpm; and (

**b**) hot water temperature within 42–48 °C with the power consumption pattern.

**Figure 23.**Objective function with iteration of the hybrid particle swarm optimization (PSO)-ANN for different population sizes.

Parameter | Value | Type |
---|---|---|

Number of inputs | 5 | ANN inputs |

Number of outputs | 4 | ANN outputs |

Number of hidden layers | 2 | ANN hidden layer |

Number of neurons in hidden layer N1 | 6 | Obtained from LSA |

Number of neurons in hidden layer N2 | 4 | Obtained from LSA |

Number of iterations | 1000 | ANN iterations |

Learning rate | 0.6175 | Obtained from LSA |

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**MDPI and ACS Style**

Ahmed, M.S.; Mohamed, A.; Homod, R.Z.; Shareef, H.
Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy. *Energies* **2016**, *9*, 716.
https://doi.org/10.3390/en9090716

**AMA Style**

Ahmed MS, Mohamed A, Homod RZ, Shareef H.
Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy. *Energies*. 2016; 9(9):716.
https://doi.org/10.3390/en9090716

**Chicago/Turabian Style**

Ahmed, Maytham S., Azah Mohamed, Raad Z. Homod, and Hussain Shareef.
2016. "Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy" *Energies* 9, no. 9: 716.
https://doi.org/10.3390/en9090716