This paper presents the development of a heuristic-based algorithm for a Home
Electric Energy Management System (HEEMS). The novelty of the proposal resides
in the fact that solutions of the Pareto front, minimizing both the energy
consumption and cost, are obtained by a Genetic Algorithm (GA) considering the
renewable energy availability as well as the user activity level (AL) inside the
house. The extensive solutions search characteristic of the GAs is seized to avoid
the calculation of the full set of Pareto front solutions, i.e., from a reduced set of
non-dominated solutions in the Pareto sense, an optimal solution with the best
fitness is obtained, reducing considerably the computational time. The HEEMS
considers models of the air conditioner, clothes dryer, dishwasher, electric stove,
pool pump, and washing machine. Models of the wind turbine and solar PV
modules are also included. The wind turbine model is written in terms of the
generated active power exclusively dependent on the incoming wind profiles. The
solar PV modules model accounts for environmental factors such as ambient
temperature changes and irradiance profiles. The effect of the energy storage unit
on the energy consumption and costs is evaluated adapting a model of the device
considering its charge and discharge ramp rates. The proposed algorithm is
implemented in the Matlab platform and its validation is performed by comparing
its results to those obtained by a freeware tool developed for the energy
management of smart residential loads. Also, the evaluation of the performance of
the proposed HEEMS is carried out by comparing its results to those obtained
when the multi-objective optimization problem is solved considering weights
assigned to each objective function. Results showed that considerable savings are
obtained at reduced computational times. Furthermore, with the calculation of only
one solution, the end-user interaction is reduced making the HEEMS even more
manageable than previously proposed approaches.

제 1편 : MATLAB 기본편
1. MATLAB 기본사용편 ···················· 003
1.1 MATLAB 시작하기 ·························· 003
명령창(command Window)에서의 입력 005
도움말(Help)의 이용 ······························· 007
1.2 입력 오류의 수정 ····························· 008
계산의 중지 ·············································· 009
MATLAB 종료하기 ································· 009
1.3 연산과 변수의 할당 ·························· 009
연산자 우선순위 ······································· 011
내장함수 ···················································· 012
1.4 데이터의 표현 ··································· 013
1.5 변수의 처리 ······································· 015
변수 이름 ·················································· 015
clear 명령어 ············································· 016
특수변수와 정수 ······································· 017
whos 명령어 ············································ 017
1.6 벡터와 행렬 ······································· 018
벡터 ··························································· 018
행렬 ·························································· 023
스크린 출력과 억제 ································· 024
1.7 랜덤(Random)수와 복소수 ·············· 025
랜덤 수 ····················································· 025
복소수 ······················································· 027
1.8 기호를 이용한 연산 ·························· 028
기호식에서의 치환 ··································· 029
1.9 코드 파일 ·········································· 030
스크립트 코드 파일 ································· 030
코멘트의 추가 ·········································· 032
함수 코드 파일 ···································· 033
사용자 정의함수 ······································ 036
1.10 간단한 그래프의 생성 ····················· 037
ezplot을 이용한 그래프 ·························· 037
plot을 이용한 그래프 ·························· 039
3차원 그래프 ··········································· 042
1.11 MATLAB과 엑셀(Excel)의 접속 043 엑셀 데이터 불러오기 ····························· 043
데이터 가져오기 옵션 ························· 046
스크립트 생성 옵션 ································· 049
함수 생성 옵션 ········································ 049
생성된 데이터를 엑셀파일로 저장하기 ·· 050

제 2편 : 연구논문
A Heuristic Home Electric Energy Management System Considering
Renewable Energy Availability

1. Introduction 52
2. Optimization Model 54
3. Multi-Objective Genetic Algorithm 59
4. Results 61
5. Conclusions 68
6. References 69