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Purpose: This study aimed to enhance the understanding of soccer match peak demands by describing worst-case scenario (WCS) and time spent above 80% and 90% of the WCS for total distance (TD) and high-speed running (HSR). The investigation considered playing level (first team vs under-19 [U19] team) and playing position (center backs, fullbacks, midfielders, and forwards) to assess how WCS and the time spent above specific thresholds vary across different populations. Methods: Data from 31 players in a professional Italian soccer club were collected during the 2022–23 season. Microtechnology devices tracked physical activity during matches. Players were categorized by position, and WCS was determined using rolling averages over a 1-minute period. Time spent above 80% and 90% of WCS for TD and HSR was calculated. Results: The U19 team exhibited higher HSR WCS compared with the first team (∼63 m·min−1 vs ∼56 m·min−1). Midfielders recorded the highest TD WCS (∼208 m·min−1), and forwards exhibited the highest HSR WCS (∼70 m·min−1). The first team spent significantly more time above 80% (∼6 min) and 90% (∼1 min) of TD WCS. Midfielders spent significantly more time above the 80% (∼7 min) of TD WCS, while forwards above the 80% (∼2 min) of HSR WCS. Conclusions: The study emphasizes that WCS used alone may not sufficiently capture real match intensity. Considering the time spent above specific thresholds provides additional insights (ie, between-levels differences and position). Practitioners should consider both WCS and time spent above thresholds for individualized training prescriptions, reflecting differences in playing roles. [ABSTRACT FROM AUTHOR]

Concerns around reliability in China’s electricity sector have rekindled interest in a traditional solution: building more coal-fired generation. However, over the past decade China’s electricity sector has seen significant changes in supply costs, demand patterns, and regulation and markets over the past decade, with falling costs for renewable and storage generation, “peakier” demand, and the creation of initial wholesale markets. These changes suggest that traditional approaches to evaluating the economics of different supply options may be outdated. This paper illustrates how a net capacity cost metric – fixed costs minus net market revenues – might be better suited to evaluating supply options in China. Using a simplified example with recent resource cost data, the paper illustrates how, with a net capacity cost metric, solar PV and electricity storage may be a more cost-effective option for meeting demand growth than coal-fired generation.

Australia, like many other countries around the world, is undergoing a transition toward net zero emissions. It requires changes and development in many sectors, which not only bring benefits but also challenges. The rapid growth in renewable energy sources (RESs) is necessary to decarbonise electricity generation but negatively affects grid stability. Residential buildings also contribute to this issue through specific load profiles and the high penetration of rooftop photovoltaic (PV) installations. Maintaining grid balance will be crucial for further emissions reductions. One of the potential solutions can be the replacement of conventional heating and cooling systems in houses with solutions capable of storing energy and shifting the electrical load. As presented in this paper, heat pumps and hydronic systems can significantly improve the electrical load of a typical South Australian household when they are controlled by algorithms reacting to the current grid conditions and household-generated electricity compared to conventional solutions. TRNSYS 18 simulations of air source and ground source heat pump systems with smart control based on measured electricity consumption and domestic hot water usage data showed the possibility of total energy consumption reduction, shifting the load from peak periods towards periods of excessive RES generation and increasing self-consumption of rooftop PV electricity. These improvements reduce the amount of emissions generated by such a household and allow for further development of other sectors. [ABSTRACT FROM AUTHOR]

Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, consequently leading to a reduction in carbon footprint. Accurately forecasting peak demand in commercial buildings provides benefits to both the suppliers and consumers by enhancing efficiency in electricity production and minimizing energy waste. Precise predictions of energy peaks enable the implementation of proactive peak-shaving strategies, the effective scheduling of battery response, and an enhancement of smart grid management. The current research on peak demand for commercial buildings has shown a gap in addressing timestamps for peak consumption incidents. To bridge the gap, an Energy Peaks and Timestamping Prediction (EPTP) framework is proposed to not only identify the energy peaks, but to also accurately predict the timestamps associated with their occurrences. In this EPTP framework, energy consumption prediction is performed with a long short-term memory network followed by the timestamp prediction using a multilayer perceptron network. The proposed framework was validated through experiments utilizing real-world commercial supermarket data. This evaluation was performed in comparison to the commonly used block maxima approach for indexing. The 2-h hit rate saw an improvement from 21% when employing the block maxima approach to 52.6% with the proposed EPTP framework for the hourly resolution. Similarly, the hit rate increased from 65.3% to 86% for the 15-min resolution. In addition, the average minute deviation decreased from 120 min with the block maxima approach to 62 min with the proposed EPTP framework with high-resolution data. The framework demonstrates satisfactory results when applied to high-resolution data obtained from real-world commercial supermarket energy consumption. [ABSTRACT FROM AUTHOR]

This research brings a new analysis method for a continuous water supply distribution network. The number of house service connections in different story buildings, rather than the nodal peak demand, shall be accounted for in the analysis. This work aims to consider the flow when pipes are opened in the house plumbing systems. The approach deviates from a traditional peak demand-based analysis of the water distribution network. The analysis gives the flow rate at each nodal point that could be observed in the different story buildings. The methodology is applied to a hypothetical network and shows how much flow and nodal pressure can occur when different percentages of consumers are in an active state. This study indicates that the peak demand-based sizing of the supply pipes could have a deficient capacity under real scenarios. The proposed analysis method will help to understand the actual behavior of the network.

This research brings a new analysis method for a continuous water supply distribution network. The number of house service connections in different story buildings, rather than the nodal peak demand, shall be accounted for in the analysis. This work aims to consider the flow when pipes are opened in the house plumbing systems. The approach deviates from a traditional peak demand-based analysis of the water distribution network. The analysis gives the flow rate at each nodal point that could be observed in the different story buildings. The methodology is applied to a hypothetical network and shows how much flow and nodal pressure can occur when different percentages of consumers are in an active state. This study indicates that the peak demand-based sizing of the supply pipes could have a deficient capacity under real scenarios. The proposed analysis method will help to understand the actual behavior of the network. [ABSTRACT FROM AUTHOR]

With the rapid development of the civil aviation industry, the number of flights has increased rapidly. However, the availability of flight slot resources remains limited, and how to allocate flight slot resources effectively has been a hot research topic in recent years. A comprehensive flight slot optimization method can significantly enhance the rationality of the allocation results. The effective allocation of flight slot is the key to improving the operational efficiency of the multi-airport system. We will optimize the flight schedule of the entire multi-airport system considering the fairness of each airport in it. The optimization results will provide an important reference for the reasonable allocation of flight slot within the multi-airport system. Based on the operation characteristics of the multi-airport system, we have established a multi-objective flight slot allocation optimization model. In this model, we set the airport capacity limit, shared waypoint capacity limit and aircraft turnaround time limit as the constraints. The optimization goal of the model is to minimize total flight schedule displacement and the maximum deviation of fairness from the absolute fairness. Gurobi solver is used to solve the model. We have innovatively incorporated the rolling capacity constraint method into our model to ensure more accurate flight slot allocation results. The Beijing-Tianjin-Hebei regional multi-airport system is selected as an example to verify the above model, and the flight slot optimization results have successfully met the fairness goal. The comparative analysis has demonstrated that the rolling capacity constraint method significantly improves the accuracy of solution results, leading to more stable flight slot allocation. The results also prove that the flight slot allocation method of multi-airport system based on absolute fairness of peak demand can improve the fairness of the allocation results. To achieve a higher level of fairness, we have found that the peak-demand based fairness method requires a smaller slot displacement compared to the non-peak demand-based method. Through the optimization of flight slot of the multi-airport system, the coordination between airports can be significantly improved. It can provide a new solution for the efficient operation of the multi-airport system.

Previous research has recognized that the value of measures that reduce electricity usage depends upon the timing of the savings generated, but the lack of hourly savings shapes has limited the demonstration of this concept. We develop empirical hourly savings shapes for residential space-conditioning measures from nearly 18,000 efficiency projects in California and show how they combine with the diurnal and seasonal variation in electricity system costs. We find that these measures (cooling replacements; windows, doors, and skylights; and other envelope measures) tend to save electricity when system costs are highest and that the hourly savings account for 1.4–1.5 times as much value as non-time-sensitive estimates of efficiency would predict. We present these impact multipliers for each measure to quantify the additional value revealed by the time-sensitive approach. We show that this additional value is similar in an evolving electricity grid with storage, rather than natural gas generation, as the marginal resource.

Australia, like many other countries around the world, is undergoing a transition toward net zero emissions. It requires changes and development in many sectors, which not only bring benefits but also challenges. The rapid growth in renewable energy sources (RESs) is necessary to decarbonise electricity generation but negatively affects grid stability. Residential buildings also contribute to this issue through specific load profiles and the high penetration of rooftop photovoltaic (PV) installations. Maintaining grid balance will be crucial for further emissions reductions. One of the potential solutions can be the replacement of conventional heating and cooling systems in houses with solutions capable of storing energy and shifting the electrical load. As presented in this paper, heat pumps and hydronic systems can significantly improve the electrical load of a typical South Australian household when they are controlled by algorithms reacting to the current grid conditions and household-generated electricity compared to conventional solutions. TRNSYS 18 simulations of air source and ground source heat pump systems with smart control based on measured electricity consumption and domestic hot water usage data showed the possibility of total energy consumption reduction, shifting the load from peak periods towards periods of excessive RES generation and increasing self-consumption of rooftop PV electricity. These improvements reduce the amount of emissions generated by such a household and allow for further development of other sectors.

Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, consequently leading to a reduction in carbon footprint. Accurately forecasting peak demand in commercial buildings provides benefits to both the suppliers and consumers by enhancing efficiency in electricity production and minimizing energy waste. Precise predictions of energy peaks enable the implementation of proactive peak-shaving strategies, the effective scheduling of battery response, and an enhancement of smart grid management. The current research on peak demand for commercial buildings has shown a gap in addressing timestamps for peak consumption incidents. To bridge the gap, an Energy Peaks and Timestamping Prediction (EPTP) framework is proposed to not only identify the energy peaks, but to also accurately predict the timestamps associated with their occurrences. In this EPTP framework, energy consumption prediction is performed with a long short-term memory network followed by the timestamp prediction using a multilayer perceptron network. The proposed framework was validated through experiments utilizing real-world commercial supermarket data. This evaluation was performed in comparison to the commonly used block maxima approach for indexing. The 2-h hit rate saw an improvement from 21% when employing the block maxima approach to 52.6% with the proposed EPTP framework for the hourly resolution. Similarly, the hit rate increased from 65.3% to 86% for the 15-min resolution. In addition, the average minute deviation decreased from 120 min with the block maxima approach to 62 min with the proposed EPTP framework with high-resolution data. The framework demonstrates satisfactory results when applied to high-resolution data obtained from real-world commercial supermarket energy consumption.

Finding the optimal location and power capacity of traction substations is a problem in the design of DC electric power supply systems for trains. The proposed method uses evolutionary multi-objective optimization (EMO) to find the solution. By using two optimization steps: firstly, finding the Pareto optimal solutions with NSGA-ii method, and secondly, selecting from the Pareto optimal solutions by using higher-level information to obtain the best solution. The method is used to optimize 15 kilometer (km) railway power supply systems with three or four traction substations. The load sharing between the traction substations is almost the same as the minimum power capacity. Therefore, the single best solution is obtained, showing that the minimum solution requirements for the location and power capacity of the traction substations are met. [ABSTRACT FROM AUTHOR]

The digital revolution requires greater reliability from electric power systems. However, predicting the growth of electricity demand is challenging as there is still much uncertainty in terms of demographics, industry changes, and irregular consumption patterns. Machine learning has emerged as a powerful tool, particularly with the latest developments in deep learning. Such tools can predict electricity demand and, thus, contribute to better decision-making by energy managers. However, it is important to recognize that there are no efficient methods for forecasting peak demand growth. In addition, features that add complexity, such as climate change and economic growth, take time to model. Therefore, these new tools can be integrated with other proven tools that can be used to model specific system structures, such as system dynamics. This research proposes a unique framework to support decision-makers in dealing with daily activities while attentively tracking monthly peak demand. This approach integrates advances in machine learning and system dynamics. This integration has the potential to contribute to more precise forecasts, which can help to develop strategies that can deal with supply and demand variations. A real-world case study was used to comprehend the needs of the environment and the effects of COVID-19 on power systems; it also helps to demonstrate the use of leading-edge tools, such as convolutional neural networks (CNNs), to predict electricity demand. Three well-known CNN variants were studied: a multichannel CNN, CNN-LSTM, and a multi-head CNN. This study found that the multichannel CNN outperformed all the models, with an R2 of 0.92 and a MAPE value of 1.62% for predicting the month-ahead peak demand. The multichannel CNN consists of one main model that processes four input features as a separate channel, resulting in one feature map. Furthermore, a system dynamics model was introduced to model the energy sector's dynamic behavior (i.e., residential, commercial, and government demands, etc.). The calibrated model reproduced the historical data curve fairly well between 2005 and 2017, with an R2 value of 0.94 and a MAPE value of 4.8%. [ABSTRACT FROM AUTHOR]

In this research paper, the problem of scheduling the home appliances is addressed. Generally, the householder's main attention is focused on minimizing his electricity payment by moving the use of the appliance from hours of high electricity rates to hours of low electricity rates. Consequently, the problem of peak demand is created. Thus, the main target behind this work is to propose an efficient scheduling strategy so that the electricity payment is reduced, the peak demand is minimized, and the user's comfort is maintained. The proposed scheduling problem is mathematically elaborated as Mixed-Integer Linear Programming where the decision variables indicate whether the electrical appliances are switched ON/OFF. The proposed strategy is implemented in four different scenarios with 10 appliances and proved to be efficient, simple, and reliable. To scrutinize the efficiency of the suggested method, similar studies in literature are used for comparison. Findings prove that the proposed method accurately minimizes the electricity payments, the peak demand while completing the 100% task required at the end of the day with lower computational time response. [ABSTRACT FROM AUTHOR]

Phase change material (PCM)-based thermal energy storage (TES) can provide energy and cost savings and peak demand reduction benefits for grid-interactive residential buildings. Researchers established that these benefits vary greatly depending on the PCM phase change temperature (PCT), total TES storage capacity, system configuration and location and climate of the building. In this study, preliminary techno-economic performance is reported for a novel heat pump (HP)-integrated TES system using an idealized approach. A simplified HP-TES was modeled for 1 year of space heating and cooling loads for a residential building in three different climates in the United States. The vapor compression system of the HP was modified to integrate with TES, and all heat transfer to and from the TES was mediated by the HP. A single PCM was used for heating and cooling, and the PCT and TES capacity were varied to observe their effects on the building's energy consumption, peak load shifting and cost savings. The maximum reduction in electric consumption, utility cost and peak electric demand were achieved at a PCT of 30 °C for New York City and 20 °C for Houston and Birmingham. Peak energy consumption in Houston, New York City, and Birmingham was reduced by 47%, 53%, and 70%, respectively, by shifting peak load using a time-of-use utility schedule. TES with 170 MJ storage capacity allowed for maximum demand shift from on-peak to off-peak hours, with diminishing returns once the TES capacity equaled the daily building thermal loads experienced during the most extreme ambient conditions. [ABSTRACT FROM AUTHOR]

Demand-side management initiatives such as voluntary demand response provide significant energy savings in the residential sector, which is a major peak demand contributor. The potential of such savings remains unexplored in Ghanaian households due to insufficient electricity consumption data, lack of end-user behavior information and knowledge about the cost-effectiveness of such programs. This research combines 80 household survey information and energy use monitoring data of household appliances, to assess the residential demand response potential of Ghana. A bottom-up approach based on modified end-use model is used to develop aggregate hourly load curve. The estimated electricity consumption is categorized based on their degree of control to determine peak demand reduction potential for the period 2018-2050. The average daily peak load reduction ranged between 65-406 MW representing 2-14% for the considered scenarios by 2050. The results show appreciable economic viability for investment in demand response with net present value varying between 28-645 million US$. We find that price, energy security and environment signals influence end-users’ electricity use behavior. Authors observe that for energy and cost savings to be realized, utility providers and consumers need effective cooperation on information delivery and feedbacks, and consumers should be incentivized to balance the benefits.

Peak hours' electricity demand is linearly related to carbon emission in fossil fuel-dominated power generation systems like in Bangladesh. Electric autorickshaws are becoming a burden for the national power system as their charging time coincides with the national peak hours. Solar electrification of the light-duty transportation sector is the focus of this work. This study identifies the uncontrolled growth of electric autorickshaws and their power consumption time as an alarming load for the national power system. To analyze the utilization of solar potential to reduce peak hours’ demand and identify the barriers underpinning the implication of solar potential in the autorickshaw sector are also the aim of this study. The results show that approximately 4660 MWh of electricity is consumed by autorickshaws in Bangladesh per day instead of the potentialities of approximately 23,000 MWh of solar energy generation capabilities per month in this sector. Cabalistic and plausible reasons behind not implication of solar potential in the autorickshaw sector are also analyzed. This study might be helpful for the related authorities in reducing peak demand and also solving the barriers responsible for not utilizing the solar potential.

Most medium and large commercial buildings in the U.S. are subject to complex electricity tariffs that combine both Time-of-Use (TOU) energy and demand charges. This study analyses the performances of different economic Model Predictive Control (MPC) formulations, from the standpoints of monthly bill reduction, load shifting, and peak demand reduction. Simulations are performed on many simplified commercial building models, with multiple TOU demand charges, and under various summer conditions. Results show that compared to energy-only MPC, the traditional method for dealing with demand charges significantly reduces peak demand and owner bill, however, highlight a lack of load shifting capability. A proposed incremental approach is presented, which better balances the bill components in the objective function. In the case study presented, this method can improve monthly bill savings and increase load shifting during demand response events, while keeping a similarly low peak demand, compared to traditional MPC methods taking into account demand charges.

Most medium and large commercial buildings in the U.S. are subject to complex electricity tariffs that combine both Time-of-Use (TOU) energy and demand charges. This study analyses the performances of different economic Model Predictive Control (MPC) formulations, from the standpoints of monthly bill reduction, load shifting, and peak demand reduction. Simulations are performed on many simplified commercial building models, with multiple TOU demand charges, and under various summer conditions. Results show that compared to energy-only MPC, the traditional method for dealing with demand charges significantly reduces peak demand and owner bill, however, highlight a lack of load shifting capability. A proposed incremental approach is presented, which better balances the bill components in the objective function. In the case study presented, this method can improve monthly bill savings and increase load shifting during demand response events, while keeping a similarly low peak demand, compared to traditional MPC methods taking into account demand charges.

Los Angeles County (LAC) is a large urbanized region with 9.7 million residents (as of 2010) and aging infrastructure. Population forecasts indicate that LAC will become home to an additional 1.2–3.1 million residents through 2060. Additionally, climate forecasts based upon representative concentration pathway (RCP) scenarios 4.5 and 8.5 indicate that average air temperatures will increase by 1–4 °C (2–7 °F) in the region. Both of these factors are expected to result in higher summertime peak electricity demand due to growth in the number of buildings, the percentage of installed air conditioners (ACs), and the additional cooling load on those air conditioners. In order to understand potential power reliability issues, and support infrastructure planning efforts, a long-term peak demand forecast was developed using hourly residential and commercial (R&C) building energy models. Peak hour electricity demand was estimated to increase from 9.5 to 12.8 GW for R&C sectors, to 13.0–17.3 GW (2–36%) and 14.7–19.2 GW (16–51%) by 2060 for the population forecasts from the California Department of Finance and the Southern California Association of Governments respectively. While marginal increases in ambient air temperature due to climate change accounted for only 4–8% of future increases in peak demand, differences in annual maximum temperatures within the 20-year periods affected results by 40–66% indicating a high sensitivity to heat waves. Population growth of at least 1 million people is anticipated to occur mostly in the northern cities of Palmdale, Lancaster, and Santa Clarita, bringing an additional 0.4–1 GW of peak demand in those regions. Building and AC efficiency are anticipated to improve as national and state efficiency standards increase, and as older, less efficient units are replaced; this could offset some of the projected increases in peak demand. Additionally, development of shared wall, multi-family dwelling units could enable population growth of up to 3 million people without increasing peak demand.

The difference between load demand in the off-peak and peak period is usually high in the context of Nepal, and the high peak load for a short time is the main reason for insufficiency in electricity, frequent electricity outage, and poor power quality. This paper investigates the effectiveness of the time of use (TOU) strategy to reduce peak demand and to improve power quality. TOU electricity price is one of the electric demand response (DR) strategies, which may motivate the customers to reduce their consumption in peak periods and shift load in the off-peak period, which propel the electricity industry towards a higher efficiency compared to that of common flat prices. In this paper, a process based on an equal step length iteration algorithm is used to obtain an optimal period partition. Using the particle swarm optimization (PSO) algorithm, a TOU-based optimization model is proposed to find the optimal electricity price. Then, its impact on peak demand reduction and voltage fluctuation is analyzed. The impact of TOU price on peak demand reduction is investigated in the hourly load profile of Nepal, which indicates the significant reduction in peak demand. Similarly, the impact on voltage fluctuation is analyzed through a case study of the IEEE 33-bus radial distribution test system, which shows voltage profile is improved.

Water utilities strive to achieve a sustainable reduction in per capita consumption (PCC) by optimising their peak demand management strategies. Socioeconomic (SE) and socio-demographic (SD) characteristics have been proven to correlate with PCC. However, the full extent to which these characteristics underpin peak demand and PCC is yet to be fully understood. Previous work used medium resolution smart meter data from 10,000 households to discover and characterise temporal consumption patterns that underpin peak demand, identifying four distinct clusters of households, namely ''Evening Peak'' (EP), Late Morning Peak (LM), Early Morning Peak (EM) and Multiple Peak (MP). Using survey results, ''Acorn household classification'', household occupancy and UK population and household attribute data, this study attempts to draw a correlation between the four clusters and known variables of the participating households. Results have revealed a strong correlation between many endogenous attributes (particularly housing, occupancy, age, number of children and household income) and households' consumption patterns underpinning peak demand. Some 56% of families in privately rented housing show EP characteristics compared with 22% owner-occupiers and 9% social renters. EP households with teenage boys have 37% higher per household consumption (PHC) than average, while EM families with teenage girls use 47% more water in early morning showers than average.

This paper describes a novel optimization-based analysis framework to evaluate the cost benefits of retrofit programs of existing Saudi housing stocks with the main objective to reduce their electrical peak demands. The retrofit programs are developed to minimize the cost for peak demand reduction while maximizing energy use savings to select sets of energy efficiency and renewable energy technologies for various residential building prototypes prevalent in Saudi Arabia. The effectiveness of large-scale implementations of optimal retrofit packages is evaluated to assess their cost benefits as well as their effects on shaping Saudi electrical load profiles. It found that the optimal retrofits are more effective than the deployment of rooftop PV systems in reducing both peak demand and annual energy consumption for Saudi housing stock. In particular, the analysis indicates that retrofitting the housing stock with one energy efficiency measure reduces electrical peak demand at 779 USD/kW lower than that attributed to deploying rooftop PV systems. [ABSTRACT FROM AUTHOR]

Key Takeaways: Traditional cost‐of‐service structures for water rates have been based on customer classes, which can overlook the stresses put on a distribution system through peak usage and unfairly charge some customers. With more and better data, an individualized approach that ties rates to effects on the water system is possible, meaning utilities can capture each customer's water‐use patterns and tailor rates accordingly. Individualized rates enabled by smart meters can result in more equitable pricing for customers, help them understand their water use, and provide a budgetary benefit for utilities. [ABSTRACT FROM AUTHOR]

The digital revolution requires greater reliability from electric power systems. However, predicting the growth of electricity demand is challenging as there is still much uncertainty in terms of demographics, industry changes, and irregular consumption patterns. Machine learning has emerged as a powerful tool, particularly with the latest developments in deep learning. Such tools can predict electricity demand and, thus, contribute to better decision-making by energy managers. However, it is important to recognize that there are no efficient methods for forecasting peak demand growth. In addition, features that add complexity, such as climate change and economic growth, take time to model. Therefore, these new tools can be integrated with other proven tools that can be used to model specific system structures, such as system dynamics. This research proposes a unique framework to support decision-makers in dealing with daily activities while attentively tracking monthly peak demand. This approach integrates advances in machine learning and system dynamics. This integration has the potential to contribute to more precise forecasts, which can help to develop strategies that can deal with supply and demand variations. A real-world case study was used to comprehend the needs of the environment and the effects of COVID-19 on power systems; it also helps to demonstrate the use of leading-edge tools, such as convolutional neural networks (CNNs), to predict electricity demand. Three well-known CNN variants were studied: a multichannel CNN, CNN-LSTM, and a multi-head CNN. This study found that the multichannel CNN outperformed all the models, with an R2 of 0.92 and a MAPE value of 1.62% for predicting the month-ahead peak demand. The multichannel CNN consists of one main model that processes four input features as a separate channel, resulting in one feature map. Furthermore, a system dynamics model was introduced to model the energy sector’s dynamic behavior (i.e., residential, commercial, and government demands, etc.). The calibrated model reproduced the historical data curve fairly well between 2005 and 2017, with an R2 value of 0.94 and a MAPE value of 4.8%.

The economy of Saudi Arabia is primarily oil-based, and the energy system is heavily dependent on fossil fuel sources. Electrical demand in Saudi Arabia has been overgrowing due to the high rates of both population and economy. The real problem is that most of this demand has been met by polluting sources, which in turn causes severe economic and environmental challenges. Despite the abundant amount of clean and freely available renewable sources, the practice of benefiting from these sources is minimal and modest. Therefore, the country has launched a promising vision, one pillar of which is diversifying energy sources and reducing dependence on oil. Since Saudi Arabia is located within the equatorial Sun Belt area, the country is a candidate for leading the World’s renewable energy industry, particularly solar energy. This paper discusses the current energy policy in Saudi Arabia, the associated main energy-related challenges and promising opportunities, and the efforts and actions taken by the country’s policymakers to address these challenges and to take advantage of available opportunities–especially those related to electrical energy–to assure continued future prosperity for subsequent generations.

Battery energy storage systems (BESSs) have the potential to reduce end users’ electricity bills by shifting their grid demand in response to price incentives. Some electricity retailers charge end users for their peak demand in the billing cycle regardless of the time of occurrence. Yet, using BESSs to reduce demand charges is challenging, since the net demand of end users can be highly uncertain due to low aggregation and on-site renewable generation. In this article, an approximate dynamic programming (ADP) methodology is developed to control a BESS to minimize an end user’s electricity bill, which includes both an energy charge and monthly peak demand charge. To address time-varying uncertainty, the net demand is modeled using a periodic autoregressive (PAR) model, which is then used to formulate a Markov Decision Process whose objective is to minimize the sum of energy, peak demand, and battery usage costs. A backward ADP strategy is developed which is enabled by new closed-form expressions for the probability distribution of the expected stage and tail costs when a radial basis function (RBF) value function approximation (VFA) is employed. The approach is applied to real net demand data from a small Australian residential community and compared to two benchmark policies: a lookup table VFA and a model predictive control (MPC) approach. The results show that the proposed approach reduces the average monthly peak demand by about 25% (yielding an 8% reduction in the electricity bill), whereas the best-performing benchmark policy reduced the peak demand by about 17%. [ABSTRACT FROM AUTHOR]

Northern countries face a unique challenge in decarbonizing heating demands. This study compares two pathways to reduce carbon emissions from building heating by (1) replacing natural gas heaters with electric heat pumps or (2) replacing natural gas with renewable gas. Optimal annual system cost and capacity requirements for Metro Vancouver, Canada are assessed for each pathway, under nine scenarios. Results show that either pathway can be lower cost but the range of costs is more narrow for the renewable gas pathway. System cost is sensitive to heat demand, with colder temperatures favouring the renewable gas pathway and milder temperatures favouring the electrification pathway. These results highlight the need for a better understanding of heating profiles and associated energy system requirements.

Phase change material (PCM)-based thermal energy storage (TES) can provide energy and cost savings and peak demand reduction benefits for grid-interactive residential buildings. Researchers established that these benefits vary greatly depending on the PCM phase change temperature (PCT), total TES storage capacity, system configuration and location and climate of the building. In this study, preliminary techno-economic performance is reported for a novel heat pump (HP)-integrated TES system using an idealized approach. A simplified HP-TES was modeled for 1 year of space heating and cooling loads for a residential building in three different climates in the United States. The vapor compression system of the HP was modified to integrate with TES, and all heat transfer to and from the TES was mediated by the HP. A single PCM was used for heating and cooling, and the PCT and TES capacity were varied to observe their effects on the building’s energy consumption, peak load shifting and cost savings. The maximum reduction in electric consumption, utility cost and peak electric demand were achieved at a PCT of 30 °C for New York City and 20 °C for Houston and Birmingham. Peak energy consumption in Houston, New York City, and Birmingham was reduced by 47%, 53%, and 70%, respectively, by shifting peak load using a time-of-use utility schedule. TES with 170 MJ storage capacity allowed for maximum demand shift from on-peak to off-peak hours, with diminishing returns once the TES capacity equaled the daily building thermal loads experienced during the most extreme ambient conditions.

Efficient electric heat pumps that replace fossil-fueled heating systems could significantly reduce greenhouse gas emissions. However, electric heat pumps can sharply increase electricity demand, causing high utility bills and stressing the power grid. Residential neighborhoods could see particularly high electricity demand during cold weather, when heat demand rises and heat pump efficiencies fall. This paper presents the development and field demonstration of a predictive control system for an air-to-air heat pump with backup electric resistance heat. The control system adjusts indoor temperature set-points based on weather forecasts, occupancy conditions, and data-driven models of the building and heating equipment. Field tests from January to March of 2023 in an occupied, all-electric, 208 m 2 detached single-family house in Indiana, USA, included outdoor temperatures as low as − 15 ° C. On average over these tests, the control system reduced daily heating energy use by 19% (95% confidence interval: 13%–24%), energy used for backup heat by 38%, and the frequency of using the highest stage (19 kW) of backup heat by 83%. Concurrent surveys of residents showed that the control system maintained satisfactory thermal comfort. The control system could reduce the house's total annual heating costs by about $300 (95% confidence interval: 23%–34%). These real-world results could strengthen the case for deploying predictive home heating control, bringing the technology one step closer to reducing emissions, utility bills, and power grid impacts at scale. • First field deployment of predictive control for an air-to-air heat pump in a cold climate. • Review of all known field studies of supervisory control for residential HVAC. • Reduced heating energy use by 13%–24% and use of highest-stage backup heat by 83%. • Occupants reported similar thermal comfort to benchmark control system. • Discussion of deployment labor, costs, challenges, and scalability. [ABSTRACT FROM AUTHOR]

Climate change is driving urgent investments in decarbonization. One core decarbonization strategy is to electrify energy services that currently directly use fossil fuels, because electricity can be generated from zero greenhouse gas energy resources. Shifting fossil-based services to electricity, however, requires a major expansion of electricity supply and increases dependence on electricity for critical services. Home heating is a particular challenge, especially in very cold climates. Unserved heating loads can be fatal. Electrified heating is expected to drive peak loads (and thus overall grid size) due to high coincident and nondeferrable loads. This study shows that highly efficient housing presents an opportunity to simultaneously protect people and structurally reduce peak load, reducing the need for electricity supply infrastructure while increasing people’s resilience to weather extremes. This study uses seven building efficiency scenarios from the National Renewable Energy Laboratory’s End Use Saving Shapes to investigate the impact of residential building efficiency on grid size in 2050, using the example of Pierre, South Dakota as a very cold weather location that might also experience substantial new housing demand due to climate-induced human mobility. We find that the deepest efficiency electrification scenario we investigate reduces peak demand by about half relative to low-efficiency electrification. Costs of about $3900/kilowatt (kW) peak load reduction are competitive with the cost of new decarbonized supplies capable of meeting peak load, though building efficiency costs are usually privatized while supply expansion costs are distributed across ratepayers. Decarbonization scenarios suggest the US grid might need to expand by a factor of 5–8 in the next 25 years: extremely rapid growth will be needed regardless, but targets might not be reachable with inefficient end users. Residential building efficiency presents an urgent opportunity to reduce peak demand and provide safer and more resilient housing.

Peak demand plays a major role in water distribution system design. Peak demand assessment is currently facing a double challenge: shifting toward a probabilistic approach, and considering the effect of different spatial and temporal resolutions. Most probabilistic approaches so far have focused on inferring scaling laws by analyzing measurement time series for different spatial and temporal resolutions. The aim of this paper is two‐fold: (a) to present a novel analytical approach that enables to assess the short‐term variability effect on probabilistic peak demands for different spatial and temporal scales, and (b) to ease the understanding of peak demand factors thanks to a physically based (rather than an empirical) approach. This is possible by combining the principles of peak demand analysis with a microcomponent‐based (i.e., end‐use oriented) demand model that enables assessment of short‐term variability. The methodology here proposed is applied to two case studies with the purpose of validating the approach and showing its full potential. Results prove that stochastic demand models can be a powerful tool for peak demand assessment. Key Points: A novel analytical approach for assessing short‐term variability effect on peak demand at different temporal and spatial scales is presentedPeak demands are computed probabilistically, based on a microcomponent stochastic demand model that enables short‐term variability analysisThe method eases the understanding of peak demands thanks to the physically based (rather than empirical) approach [ABSTRACT FROM AUTHOR]

While space cooling currently represents less than 1% of final energy use in the residential sector of the European Union (EU-28), it was the fastest growing end-use during the 2000-15 period with a mean annual growth rate of 6% per year. Currently, little is known about factors which have driven regional air-conditioning (AC) energy consumption over time, since the literature is limited to cross-sectional studies that lack differentiation between climatic and non-climatic influences. Future projections for the EU's electricity sector may therefore neglect the potential implications of rapidly growing AC demand. We develop a novel decomposition framework, which breaks down residential space cooling energy consumption in EU-28 countries into the effect of different components from 2000 to 2015. Decomposition is extended to panel data models identifying specific drivers of space cooling's climate-sensitive components. Finally, we explore scenarios of residential AC energy consumption up to 2050 and evaluate their impact on summer time peak loads. AC diffusion was found to be the key driver of space cooling energy consumption, but this effect was partly counterbalanced by efficiency gains. While weather influences AC equipment ownership rate in EU-28 households, personal income has a larger marginal effect. In baseline scenarios, AC diffusion saturates by 2050, while modestly increasing sectoral final energy use. Still, our range of scenario values for space cooling energy consumption in 2050 exceed the majority of those originating from recently published projections. In a future renewables-driven electricity system, energy security risks may emerge from a scenario of fast AC up-take in new and renovated buildings, especially for colder European countries where modelled peak cooling electricity demand is shown to outgrow the projected expansion of solar capacity. These findings have important implications for the EU's strategy to decarbonise energy supply.

Predicting energy consumption in buildings plays an important part in the process of digital transformation of the built environment, and for understanding the potential for energy savings. This also contributes to reducing the impact of climate change, where buildings need to increase their adaptability and resilience while reducing energy consumption and maintain user comfort. The use of Internet of Things devices for monitoring and control of energy consumption in buildings can take into account user preferences, event monitoring and building optimization. Detecting peak energy demand from historical building data can enable users to manage their energy use more efficiently, while also enabling real-time response strategies (including control and actuation) to known or future scenarios. Several statistical, time series, and machine learning techniques are proposed in this work to predict electricity consumption for five different building types, by using peak demand forecasting to achieve energy efficiency. We have used several indigenous and exogenous variables with a view to test different energy forecasting scenarios. The suggested techniques are evaluated for creating predictive models, including linear Regression, dynamic regression, ARIMA time series, exponential smoothing time series, artificial neural network, and deep neural network. We conduct the analysis on an energy consumption dataset of five buildings from 2014 until 2019. Our results show that for a day ahead prediction, the ARIMA model outperforms the other approaches with an accuracy of 98.91% when executed over a 168 h (1 week) of uninterrupted data for five government buildings. [ABSTRACT FROM AUTHOR]

This article presents two low bandwidth distributed model predictive control (MPC) based algorithms for the coordinated control of residential energy storage (ES) to mitigate overvoltage and reduce peak demand along LV radial distribution feeders. Each ES unit consists of a low level controller (LLC) that utilises MPC with three distinct objective functions that can optimise for financial benefit, voltage regulation and peak shaving. These modes are activated via binary signals sent by the high level controller (HLC). The HLC, operating at the feeder level, determines if any network violations such as overvoltage or overloading are predicted to occur. Using inter-bus $\boldsymbol{P}$ , $\boldsymbol{|V|}$ and $\boldsymbol{\Delta P}$ data, the HLC determines the minimum ES units to dispatch to mitigate these issues. The result is a very low bandwidth distributed control architecture with low computational complexity, that can achieve acceptable network operating conditions while dispatching the minimum number of ES units. The proposed distributed MPC algorithms were shown to outperform decentralised control strategies for voltage regulation and peak shaving from the Australian Standards and also had significantly less computational requirements than a centralised MPC controller to achieve the same network overvoltage and peak load objectives. [ABSTRACT FROM AUTHOR]

Increasing numbers of customers intend to install batteries with PV to manage their power bill or even go off-grid. To encourage the sharing of locally generated renewable energy and suburban customers to stay on grid, a novel grid friendly neighborhood energy trading mechanism (NET) has been developed. The NET is intuitive as it uses the existing network topology as the trading medium for scalability with low computation and communication requirements. The NET is designed to account for three technical goals: reflecting losses, meeting network flow constraints, and regulating voltages A MV-LV distribution network case study shows that NET effectively achieves the three goals with low communication and fast convergence. This NET mechanism helps to enable grid friendly neighborhoods to share locally generated renewable energy in a market context. [ABSTRACT FROM AUTHOR]

• A survey was administered in QC, Canada to investigate participation in DR Programs. • Results characterized contextual factors that influence participation in DR. • Results identified significant potential to expand DR programs in the province. • Respondents believed "smart" home appliances can improve their participation in DR. • Results provided valuable insights to further develop DR programs in North America. The current push towards electrification in various sectors as a decarbonization strategy presents significant challenges for utility providers due to unintended consequences related to higher peak demand. To ensure grid reliability, utilities resort to importing costly electricity or using fossil fuels, which counteracts these decarbonization efforts. To address this issue, they introduced demand-side management strategies, known as demand-response (DR) programs, to engage customers in reducing peak demand. While commercial and industrial customers have greater load flexibility, the significance of residential buildings in managing peak demand is growing due to electrification of heating systems and the proliferation of electric vehicles (EV). However, the development of residential DR programs lags behind their potential, facing challenges such as limited customer knowledge, price elasticity, and infrastructure costs. To increase residential DR program adoption, understanding occupants' perceptions and behaviors is vital. This paper presents the results of a survey conducted in Quebec to identify participation rates, incentives, approaches, and contextual factors influencing enrolment in DR programs and participation in peak demand events. It was found that there is significant potential for expanding these programs, especially by identifying key demographic sectors that may be more likely to participate compared to the current participant breakdown. The research also uncovered contextual factors and demographic variables that statistically influenced occupant behavior during peak demand events and their impact on energy-saving actions. Notably, more than 80 % of respondents believed that having smart thermostats would facilitate their participation in DR programs. These results offer valuable insights for further development and enhancement of DR programs in the province. [ABSTRACT FROM AUTHOR]

One strategy to significantly reduce greenhouse gas emissions in end-use sectors such as building heat is to switch from fossil fuels to electricity. To date, electricity consumption follows a regular and predictable pattern; however, electrifying building heat, which is driven by ambient temperature, causes this pattern to change. Previous work has not fully addressed the implications of building heat electrification on the timing and magnitude of peak demands and on flexibility requirements of the electricity grid. In this work, a high-resolution heating demand model, featuring building stock turnover forecasts, is developed, and applied to British Columbia. The study determines energy, capacity, and flexibility requirements by computing regional energy load profiles for space and water heat. Six scenarios were developed to examine the decarbonization potential in the residential and commercial building sector, considering increased heat pump adoption, building envelope energy efficiency improvements, and the implementation of building energy codes. With an emphasis on building stock improvements, 90% heat pump penetration results in only 6% increase in electrical energy even with an annual population growth rate of 1.1%; however, this scenario leads to a 37% increase in peak electricity demand. Over the time-period considered, building energy codes and retrofit rates contribute relatively little to achieving net-zero emissions in the buildings sector, while building heat electrification has significant impacts on future flexibility requirements of the electricity grid. • A high-resolution spatiotemporal heat demand simulation model with detailed building stock considerations is introduced. • Changes of electric load are analyzed for variations in building stock evolution and envelope efficiency improvements. • Mixtures of heating technologies can be applied to examine the shape and magnitude of peak electricity demands. • With an emphasis on efficiency improvements, 90% heat pump penetration results in only 6% increase in electrical energy. • Heating electrification can increase hourly load changes by up to 144%. [ABSTRACT FROM AUTHOR]

Electricity demand for room ACs is growing very rapidly in emerging economies such as India. We estimate the electricity demand from room ACs in 2030 in India considering factors such as weather and income growth using market data on penetration of ACs in different income classes and climatic regions. We discuss the status of the current standards, labels, and incentive programs to improve the efficiency of room ACs in these markets and assess the potential for further large improvements in efficiency and find that efficiency can be improved by over 40percent cost effectively. The total potential energy savings from Room AC efficiency improvement in India using the best available technology will reach over 118 TWh in 2030; potential peak demand saving is found to be 60 GW by 2030. This is equivalent to avoiding 120 new coal fired power plants of 500 MW each. We discuss policy options to complement, expand and improve the ongoing programs to capture this large potential.

Traditional energy performance calculated using building simulation with the typical meteorological year (TMY) weather data represents the energy performance in a typical year but not necessarily the average or typical energy performance of a building in long term. Furthermore, the simulated results do not provide the range of variations due to the change of weather, which is important in building energy management and risk assessment of energy efficiency investment. This study analyzes the weather impact on peak electric demand and energy use by building simulation using 30-year actual meteorological year (AMY) weather data for three types of office buildings at two design efficiency levels across all 17 climate zones. The simulated results from the AMY are compared to those from TMY3 to determine and analyze the differences. It was found that yearly weather variation has significant impact on building performance especially peak electric demand. Energy savings of building technologies should be evaluated using simulations with multi-decade actual weather data to fully consider investment risk and the long term performance. Copyright © 2011 by IPAC'11/EPS-AG.

Introduction: Spikes in the demand for blood components represent a substantial challenge to transfusion services. Simple metrics for characterizing volatility in blood components within the hospital transfusion service have not been established. Methods: We measured the volatility of demand for blood services at a large academic urban general hospital over a 6‐month period from July 2019 to December 2019 prior to the SARS‐CoV2 pandemic. Results: Among 4416 consecutive hours assessed, there were 693 h (16%) with spikes in demand for blood components with a mean (sd) of 3.8 (2.7) spikes/day. Spikes in demand were frequently clustered. The median number of hours between spikes differed by shift (6 h for days; 3 h for evenings; 3 h for nights). The percentage of shift hours with demand spikes also differed (9% day; 19% evening; 18% night). During the study, 32,447 components were distributed to 19,431 patients. Of these, 11,819 components (36%) were distributed during hours of peak demand. Hours with a simultaneous spike in both component demand and patient demand occurred in 5% of hours or approximately once each day. Conclusion: Demand for transfusion services was highly volatile in an unpredictable fashion. We provide an approach that could be used to benchmark spikes in demand for blood services at hospitals. Consideration of the frequency, unpredictability, and magnitude of spikes in demand may be relevant for hemovigilance programs and for strategies to determine the laboratory staffing needed for good patient care. [ABSTRACT FROM AUTHOR]