Denia Kolokotsa is Associate Professor at the School of Environmental Engineering of the Technical University of Crete in Greece, President of the European Cool Roofs Council, Editor of the Elsevier Energy and Buildings Journal and former Editor-in-Chief of the Advances in Building Energy Research Journal.Nikos Kampelis works as a Research Associate at the Energy Management in the Built Environment Research (EMBER) laboratory of the Technical University of Crete. He completed his PhD on demand response for the optimal integration of loads and renewable energy in microgrids.

• Preface ixNikos KAMPELISNomenclature xvNikos KAMPELISChapter 1. Demand Response in Smart Zero Energy Buildings and Grids 1Nikos KAMPELIS1.1. Introduction 11.2. Smart and zero energy buildings 21.3. DR and smart grids 91.3.1. DR and congestion management 181.3.2. DR and AS 191.3.3. DR programs 211.3.4. Building level DR 221.3.5. District level DR and microgrids 261.3.6. ANN-based short-term power forecasting 311.4. Scientific focus of the book 321.5. Book outline and objectives 34Chapter 2. DR in Smart and Near-zero Energy Buildings: The Leaf Community 37Nikos KAMPELIS, Konstantinos GOBAKIS, Vagias VAGIAS, Denia KOLOKOTSA, Laura STANDARDI, Daniela ISIDORI, Cristina CRISTALLI, Fabio Maria MONTAGNINO, Filippo PAREDES, Pietro MURATORE, Luca VENEZIA, Marina Kyprianou DRACOU, Alaric MONTENON, Andri PYRGOU, Theoni KARLESSI, Mattheos SANTAMOURIS2.1. The Leaf Lab industrial building, AEA Italy 392.2. The Leaf House residential building, AEA Italy 41Chapter 3. Performance of Industrial and Residential Near-zero Energy Buildings 43Nikos KAMPELIS, Konstantinos GOBAKIS, Vagias VAGIAS, Denia KOLOKOTSA, Laura STANDARDI, Daniela ISIDORI, Cristina CRISTALLI, Fabio Maria MONTAGNINO, Filippo PAREDES, Pietro MURATORE, Luca VENEZIA, Marina Kyprianou DRACOU, Alaric MONTENON, Andri PYRGOU, Theoni KARLESSI, Mattheos SANTAMOURIS3.1. Materials and methods 443.1.1. Energy simulation model 453.2. Energy performance analysis 513.2.1. The Leaf Lab 513.2.2. The Leaf House 573.3. Discussion 613.4. Conclusion 63Chapter 4. HVAC Optimization Genetic Algorithm for Industrial Near-Zero Energy Building Demand Response 65Nikos KAMPELIS, Nikolaos SIFAKIS, Denia KOLOKOTSA, Konstantinos GOBAKIS, Konstantinos KALAITZAKIS, Daniela ISIDORI, Cristina CRISTALLI4.1. Methodology 664.2. GA optimization model 704.3. Model of energy cost 724.4. Results and discussion 734.4.1. Scenario 1: January 25, 2018 (winter) 744.4.2. Scenario 2: March 27, 2018 (spring) 764.4.3. Scenario 3: August 15, 2018 (summer) 774.4.4. Scenario 4: September 10, 2018 (autumn) 814.4.5. Scenario 5: September 21, 2018 (autumn) 844.4.6. Scenario 6: November 20, 2018 (winter) 844.4.7. Scenario 7: November 22, 2018 (winter) 884.4.8. Scenario 8: November 25, 2018 (winter) 884.5. Conclusion and future steps 92Chapter 5. Smart Grid/Community Load Shifting GA Optimization Based on Day-ahead ANN Power Predictions 95Nikos KAMPELIS, Elisavet TSEKERI, Denia KOLOKOTSA, Konstantinos KALAITZAKIS, Daniela ISIDORI, Cristina CRISTALLI5.1. Infrastructure and methods 1005.2. Day-ahead GA cost of energy/load shifting optimization based on ANN hourly power predictions 1045.3. ToU case study 1065.3.1. ANN-based predictions 1065.3.2. GA optimization results 1125.4. DA real-time case study 1215.4.1. ANN-based predictions 1215.4.2. Combined ANN prediction/GA optimization results 1265.5. Limitations of the proposed approach 1395.6. Conclusion 139Conclusions and Recommendations 143Nikos KAMPELISReferences 147List of Authors 163Index 167

Denia Kolokotsa is Associate Professor at the School of Environmental Engineering of the Technical University of Crete in Greece, President of the European Cool Roofs Council, Editor of the Elsevier Energy and Buildings Journal and former Editor-in-Chief of the Advances in Building Energy Research Journal.Nikos Kampelis works as a Research Associate at the Energy Management in the Built Environment Research (EMBER) laboratory of the Technical University of Crete. He completed his PhD on demand response for the optimal integration of loads and renewable energy in microgrids.

• Preface ixNikos KAMPELISNomenclature xvNikos KAMPELISChapter 1. Demand Response in Smart Zero Energy Buildings and Grids 1Nikos KAMPELIS1.1. Introduction 11.2. Smart and zero energy buildings 21.3. DR and smart grids 91.3.1. DR and congestion management 181.3.2. DR and AS 191.3.3. DR programs 211.3.4. Building level DR 221.3.5. District level DR and microgrids 261.3.6. ANN-based short-term power forecasting 311.4. Scientific focus of the book 321.5. Book outline and objectives 34Chapter 2. DR in Smart and Near-zero Energy Buildings: The Leaf Community 37Nikos KAMPELIS, Konstantinos GOBAKIS, Vagias VAGIAS, Denia KOLOKOTSA, Laura STANDARDI, Daniela ISIDORI, Cristina CRISTALLI, Fabio Maria MONTAGNINO, Filippo PAREDES, Pietro MURATORE, Luca VENEZIA, Marina Kyprianou DRACOU, Alaric MONTENON, Andri PYRGOU, Theoni KARLESSI, Mattheos SANTAMOURIS2.1. The Leaf Lab industrial building, AEA Italy 392.2. The Leaf House residential building, AEA Italy 41Chapter 3. Performance of Industrial and Residential Near-zero Energy Buildings 43Nikos KAMPELIS, Konstantinos GOBAKIS, Vagias VAGIAS, Denia KOLOKOTSA, Laura STANDARDI, Daniela ISIDORI, Cristina CRISTALLI, Fabio Maria MONTAGNINO, Filippo PAREDES, Pietro MURATORE, Luca VENEZIA, Marina Kyprianou DRACOU, Alaric MONTENON, Andri PYRGOU, Theoni KARLESSI, Mattheos SANTAMOURIS3.1. Materials and methods 443.1.1. Energy simulation model 453.2. Energy performance analysis 513.2.1. The Leaf Lab 513.2.2. The Leaf House 573.3. Discussion 613.4. Conclusion 63Chapter 4. HVAC Optimization Genetic Algorithm for Industrial Near-Zero Energy Building Demand Response 65Nikos KAMPELIS, Nikolaos SIFAKIS, Denia KOLOKOTSA, Konstantinos GOBAKIS, Konstantinos KALAITZAKIS, Daniela ISIDORI, Cristina CRISTALLI4.1. Methodology 664.2. GA optimization model 704.3. Model of energy cost 724.4. Results and discussion 734.4.1. Scenario 1: January 25, 2018 (winter) 744.4.2. Scenario 2: March 27, 2018 (spring) 764.4.3. Scenario 3: August 15, 2018 (summer) 774.4.4. Scenario 4: September 10, 2018 (autumn) 814.4.5. Scenario 5: September 21, 2018 (autumn) 844.4.6. Scenario 6: November 20, 2018 (winter) 844.4.7. Scenario 7: November 22, 2018 (winter) 884.4.8. Scenario 8: November 25, 2018 (winter) 884.5. Conclusion and future steps 92Chapter 5. Smart Grid/Community Load Shifting GA Optimization Based on Day-ahead ANN Power Predictions 95Nikos KAMPELIS, Elisavet TSEKERI, Denia KOLOKOTSA, Konstantinos KALAITZAKIS, Daniela ISIDORI, Cristina CRISTALLI5.1. Infrastructure and methods 1005.2. Day-ahead GA cost of energy/load shifting optimization based on ANN hourly power predictions 1045.3. ToU case study 1065.3.1. ANN-based predictions 1065.3.2. GA optimization results 1125.4. DA real-time case study 1215.4.1. ANN-based predictions 1215.4.2. Combined ANN prediction/GA optimization results 1265.5. Limitations of the proposed approach 1395.6. Conclusion 139Conclusions and Recommendations 143Nikos KAMPELISReferences 147List of Authors 163Index 167

Denia Kolokotsa is Associate Professor at the School of Environmental Engineering of the Technical University of Crete in Greece, President of the European Cool Roofs Council, Editor of the Elsevier Energy and Buildings Journal and former Editor-in-Chief of the Advances in Building Energy Research Journal.Nikos Kampelis works as a Research Associate at the Energy Management in the Built Environment Research (EMBER) laboratory of the Technical University of Crete. He completed his PhD on demand response for the optimal integration of loads and renewable energy in microgrids.

• Preface ixNikos KAMPELISNomenclature xvNikos KAMPELISChapter 1. Demand Response in Smart Zero Energy Buildings and Grids 1Nikos KAMPELIS1.1. Introduction 11.2. Smart and zero energy buildings 21.3. DR and smart grids 91.3.1. DR and congestion management 181.3.2. DR and AS 191.3.3. DR programs 211.3.4. Building level DR 221.3.5. District level DR and microgrids 261.3.6. ANN-based short-term power forecasting 311.4. Scientific focus of the book 321.5. Book outline and objectives 34Chapter 2. DR in Smart and Near-zero Energy Buildings: The Leaf Community 37Nikos KAMPELIS, Konstantinos GOBAKIS, Vagias VAGIAS, Denia KOLOKOTSA, Laura STANDARDI, Daniela ISIDORI, Cristina CRISTALLI, Fabio Maria MONTAGNINO, Filippo PAREDES, Pietro MURATORE, Luca VENEZIA, Marina Kyprianou DRACOU, Alaric MONTENON, Andri PYRGOU, Theoni KARLESSI, Mattheos SANTAMOURIS2.1. The Leaf Lab industrial building, AEA Italy 392.2. The Leaf House residential building, AEA Italy 41Chapter 3. Performance of Industrial and Residential Near-zero Energy Buildings 43Nikos KAMPELIS, Konstantinos GOBAKIS, Vagias VAGIAS, Denia KOLOKOTSA, Laura STANDARDI, Daniela ISIDORI, Cristina CRISTALLI, Fabio Maria MONTAGNINO, Filippo PAREDES, Pietro MURATORE, Luca VENEZIA, Marina Kyprianou DRACOU, Alaric MONTENON, Andri PYRGOU, Theoni KARLESSI, Mattheos SANTAMOURIS3.1. Materials and methods 443.1.1. Energy simulation model 453.2. Energy performance analysis 513.2.1. The Leaf Lab 513.2.2. The Leaf House 573.3. Discussion 613.4. Conclusion 63Chapter 4. HVAC Optimization Genetic Algorithm for Industrial Near-Zero Energy Building Demand Response 65Nikos KAMPELIS, Nikolaos SIFAKIS, Denia KOLOKOTSA, Konstantinos GOBAKIS, Konstantinos KALAITZAKIS, Daniela ISIDORI, Cristina CRISTALLI4.1. Methodology 664.2. GA optimization model 704.3. Model of energy cost 724.4. Results and discussion 734.4.1. Scenario 1: January 25, 2018 (winter) 744.4.2. Scenario 2: March 27, 2018 (spring) 764.4.3. Scenario 3: August 15, 2018 (summer) 774.4.4. Scenario 4: September 10, 2018 (autumn) 814.4.5. Scenario 5: September 21, 2018 (autumn) 844.4.6. Scenario 6: November 20, 2018 (winter) 844.4.7. Scenario 7: November 22, 2018 (winter) 884.4.8. Scenario 8: November 25, 2018 (winter) 884.5. Conclusion and future steps 92Chapter 5. Smart Grid/Community Load Shifting GA Optimization Based on Day-ahead ANN Power Predictions 95Nikos KAMPELIS, Elisavet TSEKERI, Denia KOLOKOTSA, Konstantinos KALAITZAKIS, Daniela ISIDORI, Cristina CRISTALLI5.1. Infrastructure and methods 1005.2. Day-ahead GA cost of energy/load shifting optimization based on ANN hourly power predictions 1045.3. ToU case study 1065.3.1. ANN-based predictions 1065.3.2. GA optimization results 1125.4. DA real-time case study 1215.4.1. ANN-based predictions 1215.4.2. Combined ANN prediction/GA optimization results 1265.5. Limitations of the proposed approach 1395.6. Conclusion 139Conclusions and Recommendations 143Nikos KAMPELISReferences 147List of Authors 163Index 167