Preface |
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Nomenclature |
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xv | |
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Chapter 1 Demand Response in Smart Zero Energy Buildings and Grids |
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1 | (36) |
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1 | (1) |
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1.2 Smart and zero energy buildings |
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2 | (7) |
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9 | (23) |
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1.3.1 DR and congestion management |
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18 | (1) |
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19 | (2) |
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21 | (1) |
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22 | (4) |
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1.3.5 District level DR and microgrids |
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26 | (5) |
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1.3.6 ANN-based short-term power forecasting |
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31 | (1) |
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1.4 Scientific focus of the book |
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32 | (2) |
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1.5 Book outline and objectives |
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34 | (3) |
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Chapter 2 DR in Smart and Near-zero Energy Buildings: The Leaf Community |
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37 | (6) |
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2.1 The Leaf Lab industrial building, AEA Italy |
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39 | (2) |
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2.2 The Leaf House residential building, AEA Italy |
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41 | (2) |
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Chapter 3 Performance of Industrial and Residential Near-zero Energy Buildings |
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43 | (22) |
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3.1 Materials and methods |
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44 | (7) |
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3.1.1 Energy simulation model |
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45 | (6) |
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3.2 Energy performance analysis |
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51 | (10) |
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51 | (6) |
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57 | (4) |
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61 | (2) |
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63 | (2) |
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Chapter 4 HVAC Optimization Genetic Algorithm for Industrial Near-Zero Energy Building Demand Response |
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65 | (30) |
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66 | (4) |
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4.2 GA optimization model |
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70 | (2) |
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72 | (1) |
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4.4 Results and discussion |
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73 | (19) |
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4.4.1 Scenario 1: January 25, 2018 (winter) |
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74 | (2) |
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4.4.2 Scenario 2: March 27, 2018 (spring) |
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76 | (1) |
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4.4.3 Scenario 3: August 15, 2018 (summer) |
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77 | (4) |
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4.4.4 Scenario 4: September 10, 2018 (autumn) |
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81 | (3) |
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4.4.5 Scenario 5: September 21, 2018 (autumn) |
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84 | (1) |
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4.4.6 Scenario 6: November 20, 2018 (winter) |
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84 | (4) |
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4.4.7 Scenario 7: November 22, 2018 (winter) |
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88 | (1) |
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4.4.8 Scenario 8: November 25, 2018 (winter) |
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88 | (4) |
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4.5 Conclusion and future steps |
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92 | (3) |
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Chapter 5 Smart Grid/Community Load Shifting GA Optimization Based on Day-ahead ANN Power Predictions |
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95 | (48) |
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5.1 Infrastructure and methods |
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100 | (4) |
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5.2 Day-ahead GA cost of energy/load shifting optimization based on ANN hourly power predictions |
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104 | (2) |
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106 | (15) |
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5.3.1 ANN-based predictions |
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106 | (6) |
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5.3.2 GA optimization results |
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112 | (9) |
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5.4 DA real-time case study |
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121 | (18) |
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5.4.1 ANN-based predictions |
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121 | (5) |
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5.4.2 Combined ANN prediction/GA optimization results |
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126 | (13) |
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5.5 Limitations of the proposed approach |
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139 | (1) |
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139 | (4) |
Conclusions and Recommendations |
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143 | (4) |
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References |
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147 | (16) |
List of Authors |
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163 | (4) |
Index |
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167 | |