Estimating electricity impact profiles for building characteristics using smart meter data and mixed models

Country residential building stock electricity demand in future climate – Portuguese case study

The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock

Impacts of the co-adoption of electric vehicles and solar panel systems: Empirical evidence of changes in electricity demand and consumer behaviors from household smart meter data

Industrial electricity demand is responsible for about 31% of total electricity demand in Jordan. This paper analyzes the changes in industrial electricity demand during 1998–2005 years and identifies the factors affecting this demand. In order to gain greater insight into past electricity use changes, a Laspeyers decomposition approach was used to disaggregate changes in the electricity demand of the Jordanian industrial sector into production, structural, and efficiency effects. To accomplish the objectives of this paper, the Jordanian industrial sector was disaggregated into seven sub‐sectors: mining of chemical and fertilizer minerals, paper, plastics, petroleum, cement, iron and steel, and others industries. A major finding of this paper is that although increased industrial production caused electricity demand to increase between 1998 and 2005, significant improvements in energy efficiency and structural shift have contributed to reducing the rate of this increase.

A framework for dynamic life cycle sustainability assessment and policy analysis of built environment through a system dynamics approach

It analyses the impact of global climate change on electricity demand and its respective economic cost in buildings covering an area of 1 km by 1 km in the city of Madrid. In order to know the energy demand, meteorological information has been produced with a spatial resolution of 50 meters, taking into account the three-dimensional structure of the buildings and the land use properties around the buildings. Climate variables are dynamically downscaled from 1° to 50 m using a nesting approach. Energy simulations of buildings are implemented with the EnergyPlus model. To determine the cost of impacts, the future distribution of energy sources in the two climate scenarios analysed and the corresponding 2012 prices of the Spanish Energy Commission are taken into account. Impacts on the area’s energy demand are calculated for 2030, 2050 and 2100 versus 2011 under two IPCC global climate projections: RCP 4.5 (emission stabilization scenario) and RCP 8.5 (little effort to reduce emissions). The expected changes in electricity consumption in the year 2100 are very important. RCP 8.5 shows a strong increase in electricity demand for cooling buildings. In RCP 4.5 decreases in electricity consumption are observed (-14.37%) due to very important decreases in temperature. On average, the global climate for the year 2100 will have an impact on a typical building block in Madrid of 117918 euros per year according to scenario RCP 8.5 while in scenario RCP 4.5 110537 euros per year would be saved.

Inverse modeling is a common method to predict electricity consumption in buildings. Residential building electricity consumption patterns can vary significantly due to occupants and their sporadic energy-consuming behaviors, as well as due to variations in HVAC system types and characteristics across climate zones. However most data-driven methods in this area have been developed and evaluated using limited datasets. This points to the need for an understanding of how well data-driven models perform using residential energy consumption data from a range of locations and home types using a diverse dataset. Thus in this research, first, inverse change-point modeling methods are used to develop predictive models of monthly electricity use for 3,643 houses in four U.S. cities in three ASHRAE climate zones (2A, 4A, 5A), to evaluate the model performance. However, approximately 40% of homes did not fit within recommended criteria for change-point model development following a common model development sequence. Therefore, a modified version of the sequence, including a segmented change-point model, is then developed and evaluated. Change-point models with relaxed prerequisite criteria are also used to enable the fitting of models to a larger number of homes’ data. As a result of these modifications, the number of homes with models increased from 60% to 71%, with a goodness-of-fit improvement of 13% (RMSE) and 8% (CV-RMSE) across the datasets evaluated. The results of this work enable improved prediction of energy use across a diversity of buildings and climate zones.

Improved-performance day-ahead electricity demand forecast is important to deliver necessary information for right decision of energy management of microgrids. It supports microgrid operators and stakeholders to have better decisions on microgrid flexibility, stability and control. The available conventional forecasting methods for electricity demand at national or regional level are not effective for electricity demand forecasting in microgrids. This is due to the fact that the electricity consumption in microgrids is many times less than the regional or national demands and it is highly volatile. In this paper, an integrated Artificial Intelligence (AI) based approach consisting of Wavelet Transform (WT), Simulated Annealing (SA) and Feedforward Artificial Neural Network (FFANN) is devised for day-ahead prediction of electric power consumption in microgrids. The FFANN is the basic forecasting engine of the proposed model. The WT is utilized to extract relevant features of the target variable (electric load data series) to obtain a cluster of enhanced-feature subseries. The extracted subseries of the past values of the electric load demand data are employed as the target variables to model the FFANN. The SA optimization technique is employed to obtain the optimal values of the FFANN weight parameters during the training process. Historical information of actual electricity consumption, meteorological variables, daily variations, weekly variations, and working/non-working day indicators have been employed to develop the forecasting tool of the devised integrated AI based approach. The approach is validated using electricity demand data of an operational microgrid in Beijing, China. The prediction results are presented for future testing days with one-hour time interval. The validation results demonstrated that the devised approach is capable to forecast the microgrid electricity demand with acceptably small error and reasonably short computation time. Moreover, the prediction performance of the devised approach has been evaluated relative to other four approaches and resulted in better prediction accuracy.

An internet of things-based smart energy meter for monitoring device-level consumption of energy

Electricity consumption in residential buildings accounts for more than 30% of the total electricity consumption and thus a significant source of CO 2 emission. The growing needs to reduce the carbon emission and improve the energy efficiency of the residential buildings, the development of an accurate electricity consumption model is becoming imperative. Although many energy models for the residential buildings are already available to forecast energy consumption, most of them are designed only for specific regions. Moreover, the availability of smart meters and sensor have made possible to create models based on the energy consumption of the end-user appliances. This paper aims to highlight the different challenges associated with the modeling of residential electricity consumption.

Electricity consumption in commercial and residential buildings account for around 70 percent of the total electricity consumption in the United States. Through advanced load identification and management technologies, electric energy consumption and carbon emissions in buildings can be reduced by providing fine management of energy usage in an efficient way. This paper gives a state-of-the-art review of monitoring and identification methods for electric loads in commercial and residential buildings, compares their applicability and accuracy on different kinds of loads, and updates possible direction for future research.

This paper forecasts the future share of renewable energy generation in the Jeju power system by jointly considering the expansion of renewable energy facilities and the growth of electricity demand. To this end, multiple electricity demand models are developed based on the Basic Plan for Electricity Supply and Demand and are combined with renewable energy capacity plans to analyze annual and time-of-day generation shares. In addition, the variation in the share of renewable energy generation is compared under different operating conditions of HVDC with the mainland power system.<br/>The results show that, although renewable energy generation increases, its share in total electricity supply exhibits different trends depending on changes in electricity demand, and that HVDC operating conditions have a significant impact on renewable energy hosting capacity. Based on these findings, this paper provides insights that can be used to support future power system operation and energy policy formulation aimed at expanding renewable energy.

Real-time pricing with ramping costs: A new approach to managing a steep change in electricity demand

The large airport terminal is the city's transportation hub and logistics center, and its building electricity consumption is twice or even higher than that of general large public buildings. Energy-saving and consumption reduction of the terminal building is not only the need to improve the operational efficiency and reduce the operation cost of the airport but also the need to realize the goal of "double carbon" in China. In this study, a large airport terminal in Beijing is taken as the research object. According to the characteristics of its main electrical equipment system, and the measured data of passenger flow from January 2020 to February 2023, combined with Pearson correlation analysis, K-means cluster analysis and multiple regression analysis, the influence and correlation characteristics of building scale, passenger flow and outdoor meteorological parameters on its electricity consumption are analyzed. The results show that: 1) The daily electricity consumption of the terminal can be analyzed by five levels according to the daily passenger flow, namely, 10,000 < N ≤ 30,000,30,000 < N ≤ 50,000, 50,000 < N ≤ 70,000, 70,000 < N ≤ 100,000, and 100,000 < N ≤ 130,000; 2) The electricity consumption of the terminal can be divided into three categories: basic electricity consumption related to the building scale of the terminal, variable electricity consumption I related to passenger flow, and variable electricity consumption II related to outdoor air temperature and passenger flow. Based on this, the terminal's daily electricity consumption prediction model is constructed, and the measured data verify the model's effectiveness. 3) Put forward the evaluation reference value of the daily electricity consumption of different passenger flow in this terminal, When the year passenger flow reaches the design value of 45 million, the normal daily electricity consumption level ranges from 0.44 to 0.48 kW·h/(m2·day). The research results can provide methods and evaluation reference for electricity consumption prediction, and accurate control of electricity consumption of main equipment systems in airport terminal.

&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;&amp;amp;#160;&amp;lt;/strong&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The need to balance supply and demand has become an important policy concern in a context of a projected growth in global energy consumption. Based on the demand&amp;amp;#8211;temperature relationship and the ongoing global warming, climate change is expected to alter the regional patterns of electricity demand. This work evaluates the influence that climate change could exert on electricity demand patterns in Spain conditioned on the level of warming, with special attention to the seasonal occurrence of extreme demand days. For this purpose, assuming the currently observed electricity demand&amp;amp;#8211;temperature relationship holds in the future, we have generated daily time series of pseudo-electricity demand from the recent past until the late twenty-first century by using simulated temperatures from statistical downscaling of global climate model experiments.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We have found that, despite the minor warming effects on the median values of daily electricity demand, the mean values as well as the frequency and severity of extreme electricity demand days are expected to increase significantly in Spain, even for low levels of regional warming. Moreover, the occurrence of these extremes will experience a seasonal shift from winter to summer due to the projected temperature increases in both seasons. Under a high radiative forcing scenario of greenhouse gas emissions (RCP8.5), the extended summer season (June&amp;amp;#8211;September) will concentrate more than 50% of extreme electricity demand days by mid-century, increasing to 90% before the end of the century. Since these events will often be related to extreme heat, there could also be side effects that jeopardize the electricity infrastructure. Thus, this result should be considered by energy planners to ensure power supply and improve the effectiveness of the energy system.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Finally, we have shown that future changes in electricity demand could have considerable spatial heterogeneity over the country, which has strong implications for the management of the electricity system. While Spain is warming up faster than the global mean, there are some regions that will be exposed to lower warming than others. In particular, northwestern Spain will experience the seasonal shift later than the rest of the country due to the relatively mild summer temperatures and lower projected warming there.&amp;amp;#160;&amp;lt;/p&amp;gt;