The value of the objective function is not of interest and must not be understood as the costs of the storage system. The goal of the optimization is only to find a cost- and energy efficient usage of the storage system to find out the demand for the investigated technologies. After the optimization, the highest delta between the lowest and the highest storage state is the used storage capacity that would be necessary in the examined years.
It can be seen that the energy storage system becomes very large in the given approach that assumes neither efficiency improvements nor energy imports. That distinguishes this work to the studies mentioned in the introduction. Regarding the transportation sector, energy demand in Scenario 1 is by far the lowest. Storage demand is not significantly higher than in the other scenarios, so the usage of chemical fuels seems not to lower the demand for stationary storage.
That can be explained by the higher energy demand. On the other hand, in Scenario 1, the availability of second-life batteries could be higher than the demand for stationary battery storages if the given assumptions will turn out to be correct, which.
Therefore, battery-electric transport is likely to have the most beneficial impacts on a sector-coupled energy storage system. This work is based on the master thesis of Tobias Riedel which was supervised by Martin Zimmerlin. Henning, H. Quaschning, V. BMWi: Energiedaten: Gesamtausgabe. Deutscher Wetterdienst: Climate Data Center Bundesnetzagentur: Smard - Strommarktdaten.
Perner, J. Wietschel, M. Schmied, M. Technical report, Umweltbundesamt. Fischhaber, S. Riedel, T. Activities of daily living ADL are activities of individuals performed on a daily basis which are necessary for independent living at home.
ADLs are often used as a reliable indicator of the health of a person but manual assessment of ADLs is time consuming and labor intensive. Here, we report on a developed ultra-low power sensor platform for ADL detection.
We performed field trials in the residential setting to validate the sensor system and translated the knowledge to the domain of office buildings to enable user-centric building control. To that end, we tested the capability of the sensor platform to estimate the number of people present during meetings.
The results show that our sensor platform is able estimate the number of people with a mean absolute error of 1. Activities of daily living ADL refer to all activities related to self-care and independent living of an individual. Since the first publications of a standardized assessment protocol for ADLs by Katz et al. Since then, many more scales have emerged which have found application ranging from general geriatric assessments, dementia, stroke, development disorders and rehabilitation and provide reliable indicator for a persons health [2, 3].
Celler et al. Since then, the field of automatic detection of ADSs has gained significant traction. One reason lies in the simple fact that the manual assessment of ADLs is time consuming and labor intensive. Another reason for this trend lies in the advances and miniaturization of sensors and the emergence of IoT [5, 6]. A major benefit of automatic ADL detection systems is their ability for constant monitoring.
Tracking a patients behaviour patterns over long periods of time increases the chance for early detection of emergency situations [7]. A literature review by Peetoom et al. The assumption hereby is, that there is a simple mapping from room activity to ADL.
For example, PIR motion activity in the kitchen is mapped to cooking, activity in the bathroom to showering, bathing or personal hygiene. In our project we translate the knowledge in the field of ADL detection to the commercial sector.
We make the following contributions: 1 We built a ultra-low power sensor platform to measure the most important physical variables related to the most important activities. In order to decide, which ADLs provide the most relevant information, we analyzed the frequency of occurrence of activities in the most used ADL assessment scales. Based on this evaluation, we decided to focus on the activities cooking , eating , toileting and showering because those are among the most frequent.
Additionally, we decided to include sleeping because recent research suggests that sleep is a good predictor for cognitive impairment [9, 10, 11].
We then analyzed which physical parameter we have to measure to detect those activities. Table 1 provides the resulting mapping between physical parameters and activity. The final sensor platform is shown in Figure 1. The connector enables the sensor platform to be expandable with additional, highly specialized daugh-terboards. One such daughterboard was built to estimate the power consumption of appliances by measuring the residual magnetic AC field at the surface of power cords. As the main goal of our sensor platform is to provide an easy and reliable tool for detecting and tracking daily activities in the residential and office environment, we optimized the sensor platform according to the following constrains:.
Power consumption: The sensor platform was optimized for ultra-low power. Unobtrusiveness: The sensor platform was designed to be small, lightweight and unobtrusive. The final dimensions are 80x40x25mm. Simplicity: The sensor platform uses Bluetooth 5.
We use non-connectable advertisement packets to simplify the setup process. No pairing is required and multiple sensors can be installed in a short amount of time. Reliability: Sensor data are transmitted via Bluetooth 5.
Events from PIR and accelerometer are transmitted immediately when they occur. Security: Sensor data is encrypted via bit AES before transmission.
To validate the ability to detect ADLs in a residential environment, we installed a set of 13 sensor units in two apartments of two healthy participants.
During a period of 6 and 8 weeks the participants were instructed to keep a journal of their daily activities. A simple random forest classifier was trained on the dataset using a 7-fold Cross Validation methodology. The resulting classifier achieved a mean precision and recall over all tested activities of 0. To validate the sensor platform in an office environment, we conducted another two field trials in two meeting rooms where we analyzed the reliability of the system and its performance detecting the number of people.
Room-1 had a floor space of 3. Room-2 had floor space of 4. For model selection, we monitored the mean absolute error MAE for the estimated number of people on the validation data. We found good agreement with few people in the room and high deviation where several people occupied the room. The MAE of room-1 was 0. The MAE calculated only for time frames with presence amounted to 1. To test the transferability of the model we used it to predict the number of people using date of room-2 where we got a MAE for time-frames where people were present of 1.
In this poster abstract, we report on the design of an ultra-low power sensor platform for the detection of daily activities in residential and commercial sector. The sensor platfrom has proven to be a reliable tool for collecting sensor date in resi-dential and commercial settings. The developed model for people count estimation suggest some ability to generalize to similar rooms.
Never the less, the variability in the predictions is high which poses some limitations on the applicability of the predictions as an input to building control systems. Further improvements are required. C is the main author. H assisted and refined text and content. Katz, S. Journal of the American Geriatrics Society Juva, K. Age and ageing 26 5 , — Sikkes, S. Celler, B. International journal of bio-medical computing 40 2 , — Alwan, M. Alemdar, H. Computer networks 54 15 , — Stankovic, J.
Peetoom, K. Disability and Rehabilitation: Assistive Technology 10 4 , — Jelicic, M. International journal of geriatric psychiatry 17 1 , 73—77 Schmutte, T. Behavioral sleep medicine 5 1 , 39—56 Eeles, E. Reviews in Clinical Gerontology 16 1 , 59 Percentage change of MAPE for the three neural networks setups tested with datasets with different shares of prosumers. More and more prosumers will penetrate the power grid. But how do prosumers affect the accuracy of the day-ahead load forecast?
In contrast to related research on prosumers and load forecast, this paper addresses the impact of different shares of prosumers on the load forecast for areas with several households.
In order to answer this research question, the load forecast accuracies for a dataset without prosumers is compared to the ones of datasets with different shares of prosumers in an experimental setup using neural networks. A sliding window approach with lagged values up to seven days is applied. Apart from electricity consumption data weather and date data are considered.
It can therefore be concluded that prosumers decrease the accuracy of the day-ahead load forecast with neural networks. Prosumers are households, which consume their self-produced electricity [1]. But in addition, pro-sumers produce electricity on their own Footnote 1. A main problem of electricity produced from renewable sources is the intermittency [3]. In general, renewable energies have been regarded as non-controllable and unpredictable electricity sources [4].
This causes additional costs as operating reserves need to be planned and backup capacity for short term electricity production need to be available. For prosumers the electricity production from renewable sources and their electricity consumption from the grid are linked. They combine the uncertainty of the electricity production from renewable energy sources and the uncertainty of the behaviour of households with respect to their electricity consumption.
This leads to the hypothesis that it is more difficult to forecast the load for areas with a higher share of prosumers. This paper aims to answer the research question how much the day-ahead load forecast accuracy with neural networks is deteriorated or improved with an increasing share of prosumers. To simplify the load is predicted for a period of 24 hours. This paper distinguishes two ways to analyze. On the one hand, load forecast for prosumers can be done with neural networks, which have been trained on a dataset without prosumers.
This assumes that current load forecaster based on neural networks are used to forecast also in future when more and more prosumers may appear in the grid.
On the other hand, neural networks can be trained specifically for the load forecast for prosumers. This simulation answers the question if grids with a higher share of prosumers are in general more difficult to forecast.
The non-prosumer dataset was provided by a power utility of a city in Switzerland. Footnote 3 The prosumer dataset was provided by another power utility of another city in Switzerland 2. It contains 15 minutes measurements in kWh of the net electricity consumption and production of objects from the year After a data selection process, objects were left. Further inputs are the timestamp consisting of date and time, the weather including temperature, global radiation and precipitation downloaded from IDAweb from MeteoSwiss.
Additional inputs are the weekday, calendar week, month and the holidays. The paper is following a sliding window approach as it is proposed by [5, 6, 7]. The train label and further input variables are derived from the original dataset. The train label comprises the current kWh value of a point in time and the previous 95 minutes kWh values in order to predict the kWh values of 24 hours.
Further input variables are lagged. The best combinations of lagged variables are shown Table 1. The shares are calculated based on the annual electricity consumption in kWh of the households, not the number of households. Further, measurements of two different locations and years are combined. Because weekday impacts the load [8, 9], the data from the two dataset are not merged based on the date but based on the weekday. The weather data is always taken from the location of the prosumers, as their electricity production depends stronger on weather data, especially the global radiation, than the electricity consumption.
The holiday is taken from the dataset with the higher share in the merged dataset e. Feedforward neural networks consist of an input layer, one or several hidden layers and one output layer [10]. The input shape or number of neurons of the input layer is given by the number of input variables.
The number of input variables can vary according to the chosen size of the sliding window. The output shape of all the neural networks of this paper is 96 as this is the number of minutes kWh values within 24 hours corresponding to day-ahead load forecast. The definition of the number of neurons in the hidden layers is subject of the various tests performed to parametrize the neural networks.
In the network, the various layers of the model are fully connected [11]. Further the two related optimizers RMSprop and Adam algorithm are used. These three performance measures are also used to measure the accuracy of the load forecast, i. The used network parameterisation is shown in Table 2. Comparing the load forecast accuracy for datasets with different shares of pro-sumers, there are two ways how the load forecast was performed on these datasets.
In the first experimental setup the datasets with different shares of prosumers are used to perform the load forecast with the neural network that has an optimal parameterization to perform load forecast for non-prosumer datasets.
Afterwards, the trained neural network was tested with the data of the whole year of the four datasets with different shares of prosumers. In the second experimental setup, the neural networks have been trained and tested on the datasets with different shares of prosumers. Thereby, once the neural network parameterization with the best results for the prosumer dataset and once the parameterization with the best results for the non-prosumer dataset were used. It can also be observed that the load forecast accuracy is better when the neural network is trained and tested on the datasets with different shares of prosumers second and third row in Table of Figure 1 compared to the first setup, where the neural network was trained on non-prosumer data first row in Table of Figure 1.
Figure 1 illustrates the MAPE. The blue graph A represents the first setup. It can be observed that with this setup MAPE increases disproportionally fast with an increasing share of prosumers compared to the other two tests where the neural networks were trained and tested on datasets with different shares of prosumers orange B and grey graphs C. The comparison of datasets with different shares of prosumers has shown that the load forecast accuracy decreases with an increased share of prosumers.
Independent from the experiments the load forecast accuracy for prosumer datasets is lower than for non-prosumer datasets. The lowest forecast accuracy was achieved when the datasets with different shares of prosumers were tested on the neural network trained on a dataset without prosumers.
The result improved when the neural networks were trained and tested on the neural networks with different shares of prosumers. The consumption and production data are confidential. Bundesnetzagentur: Prosumer. Retrieved October 26, , from. Kronig, P. Schlussbericht, Teil 1. Validierung und Verbesserung Von Lastprognosen Projektphase 1. Gowrisankaran, G. Journal of Political Economy 4 , — Masa-Bote, D.
Applied Energy , — Khuntia, S. Rana, M. Neurocomputing , — Veit, A. Humeau, S. Schlussbericht, Teil 2. Analyse und Auswertung von Last- und Wetterdaten Projektphase 2. Goodfellow, I. Ayyadevara, V. Packt Publishing Ltd, Brimingham. In recent years, the academic community intensified research on local energy markets. Implementations in pilot projects provide first insights into different hypotheses and approaches.
This work presents a tested IT-architecture for local energy markets, which covers all necessary processes and basic functionality, namely the hardware, the market implementation, the database, and the application for the user. It consists of four modules and eight essential processes.
The IT-architecture can serve as a blueprint for future local energy market projects as it covers the basic processes and is at the same time extendable. The expansion of small renewable generation capacities in the distribution grid changes the paradigm of top-down electricity grids and causes the emergence of new microgrid concepts that allow participants to trade their residential generation with their neighbours. Due to this changing situation, there has been increasing discussion in recent years about local energy markets LEM [1, 2, 3].
An LEM adds a market layer to a microgrid that is originally a mere technical concept. On these markets, small local producers and prosumers trade with local customers e.
Currently, there are several pilot projects and a vital discussion about proper market designs and regulatory issues has emerged [5, 4]. However, the discussion is currently rather focused on market designs and concepts instead of IT-architectures.
Therefore, in this work, we present a developed and tested IT-architecture design for local energy markets in a microgrid. Its objective is to investigate the requirements, challenges and opportunities of an implemented LEM. The project is set up in a selected microgrid in the German city Landau. A local combined heat and power plant 50kW electrical and a photovoltaic system The microgrid is connected to the public grid via a single link and consists of connection points, most private households.
This connection ensures a continuous supply and allows excess energy to be fed into the public distribution grid. Initially, eight private households decided to participate in the LEM. Based on this case study, we describe the proposed IT-architecture and present an exemplary implementation, including specific technology choices. The architecture consists of four modules.
Each takes on functional tasks within the structure. First, the system has to record the load values of all participants Smart Meter Hardware. Second, the customer application requires an interface to enable interactions with the user. The participants must have access to their individual load data and be able to submit bids into the system User Application.
Third, load and bidding data have to be matched by the market mechanism Market. Fourth, the recorded and generated data of all former modules must be stored and accessible to all applications Database. Furthermore, specific processes exchange information between the different modules to ensure the operation of the overall information system.
A representation of the architecture with its modules and processes is shown in Figure 1. In the following, the functionality of each module and its processes are presented in detail. Energy trading on an LEM requires the current load profiles of all participants.
In the proposed architecture, a digi-tal electricity meter records the load data. This meter requires a communication module that allows the measured load values to be transmitted to the information system, where a database stores it. In Figure 1, process 1 displays this transfer of load data from the Smart Meter Hardware to the Database module.
Each meter is connected to a LoRa Sensor, which sends the recorded data to the network. The advantages of the LoRaWAN technology for this application are the easy installation, scalability due to the cost per sensor, and signal strength. A disadvantage is the LoRaWAN-Gateway, which represents a possible single point of failure if it is not redundantly installed.
User Application: The information system of the LEM needs a human-system interface where each participant is able to place bids on the market. In the proposed IT-architecture, the module User Application addresses these requirements. The application must be accessible by all participating users over, e. Figure 1 shows that five different processes originate from this module. Process 2. After a successful registration, the system can authenticate the user by its login data.
This is necessary for the login process 2. For security reasons, the login data is stored on a different database account database and separated from the market data market database.
The connection between the user authentication data and its individual market data is established with process 2. It links the ID of the smart meter hardware with the user login data. Based on this connection, process 2. Comparably, the user initiates process 2. In the case study project, the software partner provided a self-developed Android based application for mobile devices.
After a successful login by the user, the application receives a JSON Web Token from the account database to authenticate the user against the market database. It allows a stateless session between the application and the market database. Since end devices are often not optimized for data storage, the application sends live queries against the market database to receive the requested market data and visualize it. The application illustrates the data in different forms like charts and tables and a graphical controller allows to submit bids within specified limits.
Market: An LEM requires a market to match the local supply and demand. In the proposed architecture, the Market module consists of two components: The Mar-ketWrapper and the market mechanism.
The MarketWrapper is the first software component. Its task is to process the raw input data from the market database into bids for the market mechanism. Process 3. The market mechanism, the second software component, receives the bids, allocates them and generates transactions and market prices as outputs. These are handed over to the MarketWrapper, which hands them over to the market database process 3. These files are processed by the MarketWrapper into bids and handed over to the market mechanism.
The true values refer to the simulated power demands discussed above. These errors fall within an acceptable range and most loads have both small relative and absolute errors. The larger absolute estimation errors occur in the largest loads, but their relative estimation errors are small.
Similarly, the smaller loads have small absolute estimation errors than others, even when their relative estimation error is a little larger. Figure 18 shows the comparison between the original loads shapes namely true value and the estimated load shapes for one week for the load with the largest estimation error. This paper has presented a method to estimate unobservable highly meshed secondary networks based on available transformer measurements, standard load shapes, and monthly bills.
Experimental results of a real network in New York City show the feasibility and effectiveness of the proposed method in identifying hundreds of loads. This work indicates that the extremely challenging task, load estimation of highly meshed secondary networks, can be successfully solved by using the introduced method consisting of three steps: network reduction, load forecasting, and state estimation.
We also introduced an effective temperature reflecting various weather conditions to rectify the outdoor temperature and a weighing factor to balance the absolute and relative errors of state estimation. The proposed method is suitable for estimating loads of complex power loads in metropolitan areas, where the network topology is known and the secondary transformers are installed with measurements.
Future work will focus on the refinement of the state estimation methods used in the load estimation, such as computing complexity and the estimation robustness with respect to number of measurements, number of states, residuals in bad data detection, measurement error, parameter error, and topological error.
Even the forecast approach or optimization techniques based on membrane computing could be considered [ 56 — 60 ]. The data used in this paper come from a specific complex power network. In response to the readers' requirements, the authors would consider sharing them. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article of the Year Award: Outstanding research contributions of , as selected by our Chief Editors. Read the winning articles. Journal overview. Special Issues. Academic Editor: Zhile Yang. Received 24 Aug Revised 17 Dec Accepted 02 Jan Published 22 Jan Abstract This paper presents a load estimation method applicable to complex power networks namely, heavily meshed secondary networks based on available network transformer measurements.
Introduction Power networks are one kind of the most complex artificial network in the world. Problem Statement Low-voltage highly meshed secondary networks HMSNs are frequently used in densely populated metropolitan areas in North America to improve reliability. Figure 1. Topology of a typical secondary network. The secondary network is highly meshed. Figure 2. Schematic diagram of the secondary network in Figure 1. Figure 3. Figure 4. Actual load shape and outdoor temperature of one substation in NYC for a load shape; b outdoor temperature.
Figure 5. Relation between power consumption, temperature, and time of the day for all weekdays of a front view; b side view. Figure 6. Power-temperature relation of one substation in a power-temperature relation at 11 AM; b power-temperature relation at 2 AM. Figure 7. Figure 8. Comparison between the original load shapes and forecasted load shapes based on outdoor temperature: a envelope of the entire year; b valley; c peak.
Figure 9. Figure Relation between temperature and weather conditions: C, O, and R represent cloudy, overcast, and rain, respectively; the rest is clear. Comparison between the original load shapes and forecasted load shapes based on effective temperature: a entire year load shape envelope; b valley; c peak.
Table 1. Modified load shapes the standard load shape is residential weekday load shape. Comparisons of absolute and relative estimation errors absolute value between the true load and estimated load for all the buses at peak time PM, July 6, Comparison between original load shape and estimated load shape in one week for the load with the largest estimation error: a valley week; b peak week.
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Wehenkel L Contingency severity assessment for voltage security using non-parametric regression techniques. Specht DF A general regression neural network. Download references. Arif I. Midzor, LLC. You can also search for this author in PubMed Google Scholar. Correspondence to Arif I. Reprints and Permissions. Weather-based interruption prediction in the smart grid utilizing chronological data. Power Syst. Clean Energy 4, — Download citation. Received : 13 February Accepted : 26 December Published : 15 May Issue Date : April Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search SpringerLink Search. Download PDF. Abstract This unique study will demonstrate a combined effect of weather parameters on the total number of power distribution interruptions in a region.
Introduction Smart grid SG introduces a highly environmentally- friendly context for the digital power customers. Data analysis and processing Incorporating relevant, existing models, this study will develop novel theoretical models of the effects of common weather conditions and apply them to the problem of predicting the daily or by-shift number of interruptions in power distribution systems, and to the development of real-time interruption risk assessment capabilities.
Reported interruption causes. Full size image. Flowchart of the proposed weather-based interruption prediction method. Simulation for interruption prediction The proposed interruption prediction method which is used in this paper is based on two factors: 1 historical weather condition; 2 historical interruption data. Interruption prediction based on weather parameters This section illustrates the analysis of distribution network response based on variable weather conditions. The regression equation shown in this figure is Fig.
Variation of mean N versus average temperature. Evaluation of proposed interruption prediction method In this section, the proposed method is evaluated by simulating a neural network model using combined data from more than 10 management areas MAs. Interruption prediction vs actual number of interruptions for multiple Mas.
Conclusions Several models exist for extreme weather condition failure rates, and there are models for the baseline failure rates due to aging and other causes of equipment failure. In: Proceedings of the Canadian conference on electrical and computer engineering, vol 2, Toronto, 13—16 May , pp — [4] Orille AL, Bogarra S, Grau MA et al Fuzzy logic techniques to limit lightning surges in a power transformer. View author publications.
Additional information CrossCheck date: 4 February About this article. Copy to clipboard. Download references. The authors gratefully acknowledge the utility that supplied the data for this work, whose identification is not disclosed for reasons of confidentiality, and the contributions of their staff as domain experts. You can also search for this author in PubMed Google Scholar. Correspondence to Liviane Rego. Reprints and Permissions. Rego, L. Mean shift densification of scarce data sets in short-term electric power load forecasting for special days.
Electr Eng 99, — Download citation. Received : 14 April Accepted : 06 September Published : 18 October Issue Date : September Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search SpringerLink Search. Abstract Short-term load forecasting plays an important role to the operation of electric systems, as a key parameter for planning maintenances and to support the decision making process on the purchase and sale of electric power. References 1. Appl Energy — Article Google Scholar 2.
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