Indexing demand response potential at multiple temporal granularity using network theory based analysis

We investigate some issues arising from the introduction of multiple temporal granularities in an object-oriented data model. Although issues concerning temporal granularities have been investigated in the context of temporal relational database systems, no comparable amount of work has been done in the context of object-oriented models. Moreover, the main drawback of the existing proposals is the lack of a formal basis-which we believe is essential to manage the inherent complexity of the object-oriented data model. We provide a complete temporal object-oriented type system supporting multiple temporal granularities and we formally define the set of legal values for our type system. We then address issues related to inheritance, type refinement and substitutability.

A large percentage of data managed by a variety of different application domains has spatiotemporal characteristics. Unfortunately, traditional geographical information systems do not allow for an easy representation of temporal aspects of spatial data. Moreover, they do not usually support the representation of data at multiple levels of granularity. In this paper we present a multigranular spatiotemporal data model. Our model extends the ODMG model with multiple spatial and temporal granularities. In particular, the model allows for an uniform management of two kinds of spatiotemporal objects: moving entities (e.g. cars, planes, etc.) and temporal maps (i.e., maps representing the change over time of a given geographic area). It also provides a framework for mapping the movement of an entity such as a car onto an underlying geographic area. The model we propose relies on a standard definition of temporal granularity. On the other hand, the representation of spatial entities at multiple granularities is obtained by applying model oriented map generalization principles. In particular, we consider a set of generalization operators that guarantee topological consistency.

Managing and querying information with varying temporal granularities is an important problem in databases. Although there is a substantial body of work on temporal granularities for the relational data model (Snodgrass, 1995), a comprehensive framework is lacking for the object-oriented paradigm. To the best of our knowledge, a formal treatment of temporal queries with multiple granularities has not been considered in the literature. We make a step in this direction. We formally introduce the syntax and semantics of expressions involving data with multiple granularities, comparison between data with different granularities, and conversion of data from one granularity to another. We believe that this is an important step towards the development of an object-oriented query language that supports multiple granularities.

The demand response potential in copper production

As an intermediary between residential customers and system operators, load aggregators (LA) are responsible for integrating the residential customers' demand response (DR) potential and enabling their transactions in the day-ahead market. Profit of the market trading process mainly relies on the pre-estimated achievable DR potential, which puts forwards the necessity of its accurate forecasting. Therefore, this paper proposes a probabilistic forecasting model to forecast the day-ahead achievable DR potential at the aggregated level under an incentive-based demand response (IBDR) program. Firstly, we attempt to establish the customers' DR potential, during which a home energy management system (HEMS) is introduced to implement load adjustment for electrical appliances. Secondly, several features that may affect the DR potential are extracted, among which the more relevant ones are selected through the support vector machine recursive feature elimination (SVM-RFE) method. Finally, based on these selected features, a support vector machine (SVM) method is adopted to establish the DR potential point forecasting model, and then the probabilistic forecasting model of the aggregated DR potential is established through the superimposition of the point forecasting results and the corresponding error distribution, the latter could be estimated by the non-parametric kernel density (NKDE) method. Case studies show that a good performance could be achieved by the proposed probabilistic forecasting model and the feature selection process could significantly improve the forecasting accuracy.

Smart meter data provides rich information on customers’ energy consumption behaviors, which can be a valuable resource for evaluating customers’ demand response (DR) potential and thus can inform the distribution system expansion planning (DSEP). This paper presents a novel DSEP framework incorporating a data-driven model for a particular type of incentive-based DR called the thermostatically-controlled-load DR. The heterogeneity in individual customers’ DR potential is considered in the DSEP by leveraging the healing, ventilation, and air conditioning (HVAC) load information extracted from smart meter data. The relationship between the DR incentive and the customer participation rate is also considered so that incentives can be optimally designed for customers with different DR potentials in a differentiated way. The overall DSEP problem is formulated into a robust optimization framework to address the uncertainties from the load demands, renewable energy generations, and DR resources. Case studies show that the proposed DSEP model can substantially reduce the total expansion cost over conventional planning paradigms, demonstrating the positive role of the proposed data-driven DR model in DSEP.

T-ODMG: an ODMG compliant temporal object model supporting multiple granularity management

With the development of the times and the progress of society, the development and change of the demand response potential of China’s residents are facing unprecedented challenges. In today’s big data era, the combination of big data technology and the potential analysis of demand response of China’s residential users has become the inevitable demand of the development of the times. Therefore, in order to better make the demand potential of Chinese residents conform to the development trend of the times, this paper deeply studies the business development trend and status quo of the Internet in the demand analysis and response of residents in recent years through the technology of Internet and big data, and analyzes the potential of demand analysis and response of residents in recent years, A large number of information resources about the demand analysis and response of residential users in the new Internet era are sorted out, and the business fields of residents’ demand analysis and response are re classified. The evaluation model of influencing factors of user demand response behavior is established, and the Monte Carlo simulation calculation method is used for research. It is found that time and price is the main factors influencing the demand response behavior of typical industries. Through the analysis, the accuracy rate of the big data analysis method proposed in this paper reaches 97.3% in studying the potential of residents’ demand response.

In many real-world applications, temporal information is often imprecise about the temporal location of events (indeterminacy) and comes at different granularities. Formalisms for reasoning about events and change, such as the Event Calculus (EC) and the Situation Calculus, do not usually provide mechanisms for handling such data, and very little research has been devoted to the goal of extending them with these capabilities. In this paper, we propose TGIC (Temporal Granularity and Indeterminacy event Calculus), an approach to represent events with imprecise location and to deal with them on different timelines, based on the EC ontology.

Stochastic modelling of flexible load characteristics of split-type air conditioners using grey-box modelling and random forest method

The Multidimensional Databases (MDB) are used in the Decision Support Systems (DSS) and in Geographic Information Systems (GIS); the latter locates spatial data on the Earth’s surface and studies its evolution through time. This work presents part of a methodology to design MDB, where it considers the Conceptual and Logical phases, and with related support for multiple spatio-temporal granularities. This will allow us to have multiple representations of the same spatial data, interacting with other, spatial and thematic data. In the Conceptual phase, the conceptual multidimensional model—FactEntity (FE)—is used. In the Logical phase, the rules of transformations are defined, from the FE model, to the Relational and Object Relational logical models, maintaining multidimensional semantics, and under the perspective of multiple spatial, temporal, and thematic granularities. The FE model shows constructors and hierarchical structures to deal with the multidimensional semantics on the one hand, carrying out a study on how to structure “a fact and its associated dimensions.” Thus making up the Basic factEnty, and in addition, showing rules to generate all the possible Virtual factEntities. On the other hand, with the spatial semantics, highlighting the Semantic and Geometric spatial granularities.

Practical demand response potential evaluation of air-conditioning loads for aggregated customers

Demand Response Potential: Available when Needed?

To reduce the cost of load aggregators (LA), recent studies suggest optimizing LA’s bidding plans by utilizing the demand response (DR) potential of various flexible end consumers. With the exploding growth of IT demand, an emerging flexible end consumer, data centers, has begun to appear around the world. In this paper, we focus on utilizing the data centers’ DR potential to optimize the bidding plans of data center aggregators (DCA), which is the LA that supply power for multiple data centers. First, an optimization bidding model is formulated based on data centers’ DR potential, which consider the backup energy characteristics and workloads spatiotemporal transfer characteristics of data centers. Then, in view of the unique load characteristics of data centers, we propose a rational and quantitative compensation mechanism for the DCA to induce data centers participating in DR. In the end, simulation results are used to prove that the proposed model is beneficial to both the DCA and data centers.

The uncertainty of power load is one of the important research directions in demand response uncertainty. Accurate and effective power system load forecasting is an important prerequisite for ensuring the safety, stable operation, and normal production of the power grid. To improve the accuracy of short-term load forecasting in power systems under demand response scenarios, this paper proposes a Transformer load forecasting method that considers demand response potential. Firstly, the change law of response uncertainty with electricity price difference and consumer psychology principles are used to quantify the power demand response results under different probability conditions. Then, Transformer neural networks are used to extract features from user historical load, temperature, electricity price, and other time series data. Finally, a multi-head self-attention mechanism is used to pay attention to the structural relationship between time series data, analyze the importance of input variables at each historical moment on the current load, and achieve high-precision prediction of user load and demand response potential. This article takes industrial users as an example to predict the power load and demand response regulation power of the general component manufacturing industry. Through comparative analysis with actual data, the effectiveness of the proposed method is verified. Compared with other existing methods, the Transformer model that considers demand response performs well in power load forecasting, providing a certain theoretical basis for evaluating the potential of demand response. The subsequent work will study the characteristics of electric, hot, and cold loads and their coupling relationships under the difference of electricity prices, and improve the forecasting performance of user loads and Demand Response Regulation power, so as to reduce the power generation and operation costs of the grid.