Efficient Demand Management Research Articles

Online forecasting using neighbor-based incremental learning for electricity markets

Abstract Electricity market forecasting is very useful for the different actors involved in the energy sector to plan both the supply chain and market operation. Nowadays, energy demand data are data coming from smart meters and have to be processed in real-time for more efficient demand management. In addition, electricity prices data can present changes over time such as new patterns and new trends. Therefore, real-time forecasting algorithms for both demand and prices have to adapt and adjust to online data in order to provide timely and accurate responses. This work presents a new algorithm for electricity demand and prices forecasting in real-time. The proposed algorithm generates a prediction model based on the k-nearest neighbors algorithm, which is incrementally updated in an online scenario considering both changes to existing patterns and adding new detected patterns to the model. Both time-frequency and error threshold based model updates have been evaluated. Results using energy demand from 2007 to 2016 and prices data for different time periods from the Spanish electricity market are reported and compared with other benchmark algorithms.

Efficient demand management for the on-time arrival problem: A convexified multi-objective approach assuming macroscopic traffic dynamics

This work proposes a novel solution approach to address the On-Time Arrival (OTA) problem, considering macroscopic traffic dynamics. In this context, all drivers who intend to use the road infrastructure are required to communicate their origin–destination pair and desired arrival time to a central scheduler prior to starting their trip. In response, the scheduler assigns a departure time and a multi-regional route to each driver to make possible their on-time arrival at the destination. The OTA problem is formulated as a nonconvex, nonlinear, multi-objective optimization problem considering two objective criteria. The first criterion aims at minimizing the travel time of all drivers in the network to prevent congestion, while the second criterion seeks to minimize the discrepancy between the desired and actual arrival time. The proposed formulation is solved efficiently through an approximated convex solution that leverages the Normal Boundary Intersection (NBI) method to efficiently generate a representative sample of the Pareto Front. Additionally, two solution methodologies based on “knee” solution and the Nash Bargaining Game is proposed which can be used to select a unique solution across all the Pareto points, that offers a reasonable trade-off between the two objective criteria. Finally, simulation results demonstrate that the proposed solution can significantly reduce congestion while ensuring that most drivers will arrive at their destination on their desired time.

Managing Healthcare Product Demand Effectively in The Post-Covid-19 Environment: Navigating Demand Variability and Forecasting Complexities

Managing the demand for healthcare products is a complex problem requiring in-depth knowledge of epidemiological trends, supply chain dynamics, and regulatory considerations. The intricate management of healthcare product demand in the post-COVID-19 era is thoroughly explored in this study. It is divided into nine sections exploring various facets of this critical field, emphasizing the persistent demand variations and difficulty in forecasting in the healthcare industry. It underscores the necessity for efficient demand management techniques in the changing healthcare environment. The significance of historical demand trends and provides examples of how studying historical data is essential for comprehending and forecasting the demand for healthcare products. Identifying seasonal fluctuations, forecasting disease outbreaks, and improving inventory management are emphasized. Investigating the difficulties in predicting the need for healthcare products, deals with problems caused by epidemiological ambiguity, supply chain breakdowns, patient demographics, regulatory adjustments, and technological advancements. The importance of data availability and quality in accurate demand forecasting is emphasized in this section. It encompasses scenario planning, resource allocation, technology adoption, supplier diversity, flexible manufacturing, collaborative and information sharing, data-driven decision-making, demand forecasting models, and strategic stockpiling. "Data Analytics and Predictive Modeling in Demand Forecasting," emphasize the function of these techniques in comprehending and forecasting demand trends. All covered are time series analysis, machine learning algorithms, epidemic predictive modeling, supply chain integration, scenario planning, optimization, risk assessment, in-the-moment monitoring, and feedback loops. "Supply Chain Resilience and Demand Response" explains why it's crucial to create robust supply chains and successfully adapt to demand changes. Supplier diversity, strategic stockpiling, agile production, real-time visibility, teamwork, data-driven decision-making, scenario planning, and regulatory flexibility are all covered

Preface

International Conference on Advanced Electrical and Energy Systems (AEES 2020) were held online during August 18-21, 2020. These conferences addressed diverse topics related to recent trends and progress made in the field of advanced energy systems.Advanced electrical and energy systems have been the main topics in research and development at the academic, industry and business level in recent year. It enables renewable generation, distributed generation, energy storage, transmission, distribution and demand management.The benefits of advanced electrical and energy systems include the enhanced reliability and resilience, higher intelligence and optimized control, decentralized operation, higher operational efficiency, more efficient demand management, and better power quality. However, all these prospective transformations also bring with them numerous challenges and opportunities. Concerns relating to global warming, caused by green house gas (GHG) emissions from various sources, have raised general awareness among the governments and the public of the need to produce energy, in particular energy in the electrical form, from renewable energy sources that do not produce GHGs.The papers presented at these conferences and included in these Proceedings cover a wide variety of topics on various aspects of advanced energy sources of the future, the focus of these series of conferences held in August 2020. We hope that they will offer the readers a good opportunity to explore the state-of-the art developments and future directions of research on these topics.On behalf of Conference CommitteesProf. Om P. Malik, University of Calgary, Canada (LFIEEE)

Machine Learning and Data Analytic Techniques in Digital Water Metering: A Review

Digital or intelligent water meters are being rolled out globally as a crucial component in improving urban water management. This is because of their ability to frequently send water consumption information electronically and later utilise the information to generate insights or provide feedback to consumers. Recent advances in machine learning (ML) and data analytic (DA) technologies have provided the opportunity to more effectively utilise the vast amount of data generated by these meters. Several studies have been conducted to promote water conservation by analysing the data generated by digital meters and providing feedback to consumers and water utilities. The purpose of this review was to inform scholars and practitioners about the contributions and limitations of ML and DA techniques by critically analysing the relevant literature. We categorised studies into five main themes: (1) water demand forecasting; (2) socioeconomic analysis; (3) behaviour analysis; (4) water event categorisation; and (5) water-use feedback. The review identified significant research gaps in terms of the adoption of advanced ML and DA techniques, which could potentially lead to water savings and more efficient demand management. We concluded that further investigations are required into highly personalised feedback systems, such as recommender systems, to promote water-conscious behaviour. In addition, advanced data management solutions, effective user profiles, and the clustering of consumers based on their profiles require more attention to promote water-conscious behaviours.

The paradox of cost recovery in heterogeneous municipal water supply systems: Ensuring inclusiveness or exacerbating inequalities?

Over the past decades ‘water for all’ has become a dominant development mantra, illustrated by global strategies like the Millennium Development Goals (2000–2015) and the Sustainable Development Goals (2015–2030). Cost-recovering tariffs have been placed at the core of these strategies on the grounds that they warrant more inclusive water services by enhancing utilities' performance, ensuring efficient demand management and empowering consumers. This paper questions these assumptions for cities in sub-Saharan Africa, where inclusive urban water services are to be achieved in a context of extreme socio-economic inequalities and the water utility provides water through heterogeneous service modalities. Drawing from empirical evidence from Maputo and Lilongwe, we conclude that in this context the implementation of full cost recovery principles may exacerbate rather than reduce inequalities in access to drinking water. Water utilities tend to outsource service provision to lower income areas to small-scale or “social” private sector. These providers apply full–cost recovery principles more rigorously, as they cannot operate in a deficit. Moreover, they are unable to (cross)-subsidize and they do not enjoy economies of scale. As a result, wealthier neighbourhoods, where the water utility provides services directly, often access water at subsidised rates, while in low income areas, where service provision is outsourced, people access lower quality services at a higher price.

Centralised grocery supply chain planning: improved exception management

Purpose – The purpose of this paper is to present an operations model for retail replenishment collaboration and identifies its expected benefits and limitations for the members of a grocery supply chain. Design/methodology/approach – A case study is conducted on a development project between a grocery wholesaler and two grocery product suppliers. Data are collected through semi-structured interviews with key respondents from four different companies. Findings – Despite advances in collaborative practices in grocery supply chains, retail store replenishment management faces challenges. In particular, demand exceptions management is a challenge in the grocery industry. A replenishment model called Collaborative Buyer-Managed Forecasting (CBMF) creates a proactive planning approach and a platform for close collaboration in the supply chain. The centralised forecasting transforms retailer sales data into a plan which serves the whole supply chain by creating one-order forecast. The CBMF model facilitates efficient demand management, improves demand responsiveness and promotes better availability of products in retail stores. Research limitations/implications – CBMF provides a replenishment planning model for the whole supply chain. It is tested to a limited extent in one supply chain. Practical implications – The study provides managers with a better understanding of the benefits of centralised forecasting and closer replenishment collaboration, especially during periods of exceptional demand. Originality/value – A new approach for managing demand in grocery supply chains with centralised forecasting is provided.

Model predictive HVAC load control in buildings using real-time electricity pricing

We propose a practical cost and energy efficient model predictive control (MPC) strategy for HVAC load control under dynamic real-time electricity pricing. The MPC strategy is built based on a proposed model that jointly minimizes the total energy consumption and hence, cost of electricity for the user, and the deviation of the inside temperature from the consumer's preference. We develop an algorithm that assigns temperature set-points (reference temperatures) to price ranges based on the consumer's discomfort tolerance index. We also design a practical parameter prediction model for the mapping between the HVAC load and the inside temperature. The prediction model and the produced temperature set-points are integrated as inputs into the MPC controller, which is then used to generate signal actions for the AC unit. To investigate and demonstrate the effectiveness of our approach, we present a simulation based experimental analysis using real-life pricing data. The experiments reveal that the MPC strategy can lead to significant reductions in overall energy consumption and cost savings for the consumer. Results suggest that by providing an efficient response strategy for the consumers, the proposed MPC strategy can enable the utility providers adopt efficient demand management policies using real-time pricing.

Full Closure or Partial Closure? Evaluation of Construction Plans for the I-5 Closure in Downtown Sacramento

Three alternative construction plans with full highway closures and limited capacity closures (partial closures) are evaluated for a large-scale freeway reconstruction project in the Sacramento metropolitan area. By using a dynamic network analysis tool, the potential impact resulting from different construction plans is estimated and analyzed measured by changes in total demand, travel delays, vehicle miles/hours traveled (VMT/VHT), fuel consumptions, and emissions. The results show that, for this Interstate 5 closure, if only user delay costs are considered, the actual full closure plan is the best. It produces considerably less total system travel delay than the two partial closure plans although it closed more lanes. This plan, however, may induce more VMT than the other two. In fact, the other two partial closure plans produce slightly fewer emissions and consume slightly less fuel. The outcome of which plan comes out on top crucially depends on two other factors, besides the chosen objective: the amount of demand reduction a plan induces and the availability of alternative routes to divert traffic away from the work zone area. Developing efficient demand management measures will be the key to improving the network performance and to reduce the emissions. Moreover, when data are available, construction costs and emissions produced by construction activities should also be included in the evaluations, in addition to the user costs considered in this study.

Competitive electricity markets with consumer subscription service in a smart grid

This paper presents a theory of consumer subscription service, unifying priority service and dynamic pricing within a two-settlement system in ways that foster efficient risk management and competitive electricity markets. Consumer subscription service entails transactions in two time periods: a forward transaction that allows consumers to select service options to hedge financial risks and a spot transaction that allows consumers to secure electricity on demand. The difference between the forward subscription and the actual consumption is settled at spot prices determined in a competitive wholesale electricity market. Priority service offers consumers choices from a menu of reliability-differentiated service options with compensatory insurance for curtailments. Following the principles of revelation and competitive consistency, the priority service menu provides consumers opportunities to choose options that are consistent with their individual preferences and demand characteristics. Combining priority service and dynamic pricing, consumer subscription service is Pareto superior to an undifferentiated service design. Overall, consumer subscription service provides consumers incentives and tools for efficient demand management fostering price-responsive demand in competitive electricity markets. It facilitates an essential linkage between competitive wholesale and retail markets in ways that would enable consumers to be engaged in the process of the industry’s transition toward a smart grid future.

Using utility information to calibrate customer demand management behavior models

In times of stress customers can help a utility by means of voluntary demand management programs if they are offered the right incentives. The incentives offered can be optimized if the utility can estimate the outage or substitution costs of its customers. This paper illustrates how existing utility data can be used to predict customer demand management behavior. More specifically, it shows how estimated customer cost functions can be calibrated to help in designing efficient demand management contracts.

Using Utility Information to Calibrate Customer Demand Management Behavior Models

In times of stress customers can help a utility by means of voluntary demand management programs if they are offered the right incentives. The incentives offered can be optimized if the utility can estimate the outage or substitution costs of its customers. This paper illustrates how existing utility data can be used to predict customer demand management behavior. More specifically, it shows how estimated customer cost functions can be calibrated to help in designing efficient demand management contracts.

Issues in resource conservation and sustainable development: Indian situation

Higher economic growth is necessary to reduce the level of poverty and improve the living standard of population. But higher economic growth requires greater use of natural resources and environment which in turn leads to their degradation and eventual decay. Increased population pressure on natural resources also contributes to their degradation. Hence, higher growth may not necessarily lead to sustainable development unless it is accompanied by environmental protection. An efficient demand management policy by emphasizing conservation and prudent use of environment can also increase the supply of reserves. One alternative is to attempt to conserve resources via community management of such resources. But community management cannot succeed in absence of appropriate property rights of the community and of women and appropriate institutions to enforce such rights. These in turn require the presence of an activist developmental state which can engage in a participatory system of environmental conservation and management.