Smart Meters: Artificial Intelligence to Support Proactive Management of Energy Consumption

Description

This book describes how equipping buildings with smart meters is essential to improve the prediction of energy costs within smart grids and to help end-users optimize their energy consumption. The book reports on the results of the European Upper Rhine INTERREG project SMI (www.smi.uha.fr), which connects artificial intelligence and micro-societal analysis. It is multidisciplinary and addresses the following aspects: social, legal, environmental, and technical. One of the critical factors for the transition to clean energy is the flexibility of the power grid. A flexible grid requires a constant flow of data about the network and its demand, on the other hand, clients who produce electrical power can be an active part of the demand response if they are informed about the power needs of their appliances. “If you cannot measure it, you cannot improve it.” This common management saying also holds true for the area energy efficiency. Without a clear understanding of their energy usage, consumers are unable to take steps to reduce their consumption. A new intelligent tool is presented that is more efficient, safe, and acceptable to consumers. Thus, users of this intelligent tool will be able to collect and predict the consumption of their electrical appliances. At the same time, the consumption information is anonymized before being relayed to the energy supplier. In parallel, new techniques will be evaluated to improve the security level of the smart meter in a highly heterogeneous network. Preface Contents Advanced State of the Art Based of Smart Meters Already Carried Out in Europe and Around the World 1 Introduction 2 Barriers and Drivers of Smart Meter Adoption 2.1 Drivers of Smart Meter Acceptance 2.2 Barriers of Smart Meter Acceptance 3 Important Factors of Social Acceptance Towards Smart Meter 3.1 Rollouts and Information 3.2 Health 4 Technology-Based Factors of Smart Meter 4.1 Communication Technology 4.2 Communication Networks 4.3 Smart Metering Worldwide References Understanding the Sources of Consumer Resistance to Smart Meters 1 Introduction 2 The Ambivalence and Limited Engagement of European Consumers Towards Smart Meters 3 Sources of Consumer Disempowerment in the Social Representations of Smart Meters 3.1 Privacy Invasion and Information Panopticon 3.2 Loss of Individual Autonomy 3.3 Rejection of Additional Measures to Reduce Energy Consumption at the Household Level 3.4 Mistrust of Energy Suppliers and Price Vulnerability 4 Recommendations on How to Improve Consumer Empowerment in the Use of Smart Meters 5 Conclusion References Smart Meters Improved by NILM 1 Introduction 2 Efficient Energy Monitoring Through Non-intrusive Load Monitoring 2.1 Load Disaggregation and Other Terms 2.2 Intrusive Versus Non-intrusive 2.3 Process Chain of Non-intrusive Load Monitoring 2.4 Appliance Categories 2.5 Event-Driven Versus Eventless Approach 3 Measurement Systems 3.1 Sensors for Non-intrusive Load Monitoring 3.2 Measurement Hardware 3.3 Public NILM Datasets 4 Feature Extraction for the Appliance Classification 4.1 Steady State and Transient State 4.2 Extraction of Features in NILM 5 Frequency Invariant Transformation of Periodic Signals 6 Artificial Intelligence for Appliance Classification 6.1 Supervised Learning 6.2 Unsupervised Learning 6.3 Semi-supervised and Online Learning 7 Conclusion References Helping Consumers to Reduce Their Energy Consumption and Greenhouse Gases Emissions: What Tool to Develop? 1 Introduction 2 Practices and Control of Energy Consumption 2.1 Analysis of Energy Consumption by Lifestyle 2.2 Framework for Analyzing Practices and Representations of Energy Demand Management (EDM) 2.3 Energy Demand Management (EDM) Measures 3 Analysis of the Tools and the Representations of the EDM 3.1 Analysis of EDM Tools 3.2 What Social Representations of Energy Management? 4 A New Tool for Ecological Reflexivity 5 Conclusion References Security Aspects of Smart Meter Infrastructures 1 Introduction 2 Security Architectures—A Comparative Overview 2.1 French Case 2.2 German Case 2.3 Swiss Case 3 Comparison with Reference Architecture 4 Security Analysis 4.1 DLMS/COSEM Protocol Description 4.2 DLMS/COSEM Security 4.3 IDIS Specifications 4.4 DLMS/COSEM Vulnerabilities 4.5 TLS with BSI Restrictions and DLMS/COSEM Comparison 5 Theoretical SMI Systems Security Comparison 6 Use Case: Secure Smart Meter Gateway 7 Recommendations References Legal Aspects of the Smart Meter Rollout in Germany, France and Switzerland 1 Introduction 2 Union Law Background 2.1 Primary Law 2.2 Secondary Law 3 The German Way—Implementation of the Smart Meter Rollout in German Law 3.1 General Information on the Smart Meter Rollout in the MsbG 3.2 The Decision of the OVG Münster from 04.03.2021 and the Consequences 3.3 Data Protection—Legal Framework of Smart Meters Between DSGVO—BDSG and LDSG as Well as the Sector-Specific Regulations of §§ 55 MsbG 3.4 Interim Conclusion 4 The French Way—Implementation of the Smart Meter Rollout in French Law 4.1 General Information on the French Transposition 4.2 Legal Framework of the Smart Meter Rollout 4.3 Data Protection Framework 4.4 Interim Conclusion 5 The Swiss Way—Implementation of the Smart Meter Rollout in Swiss Law 5.1 Definition and Legal Framework of the Smart Meter Rollout 5.2 Data Protection Legal Framework in the Data Protection Act 6 Conclusion References Technoeconomic Review of Smart Metering Applications 1 Introduction 2 Smart Meter Roll-Out 3 Smart Metering Applications 3.1 Energy Service Provider—Oriented Smart Metering Applications 3.2 Customer-Oriented Smart Metering Applications 3.3 Other Stakeholder-Oriented Smart Metering Applications 4 Current Status of Smart Metering Applications 4.1 Questionnaire 4.2 Results 5 Discussion and Conclusion References