Punctuality Predictions in Public Transportation: Quantifying the Effect of External Factors

Increasing availability of large-scale datasets for automatic vehicle location (AVL) in public transportation (PT) encouraged researchers to investigate data-driven punctuality prediction models (PPMs). PPMs promise to accelerate the mobility transition through more accurate prediction delays, increased customer service levels, and more efficient and forward-looking planning by mobility providers. While several PPMs show promising results for buses and long-distance trains, a comprehensive study on external factors\u27 effect on tram services is missing. Therefore, we implement four machine learning (ML) models to predict departure delays and elaborate on the performance increase by adding real-world weather and holiday data for three consecutive years. For our best model (XGBoost) the average MAE performance increased by 17.33 % compared to the average model performance when only trained on AVL data enriched by timetable characteristics. The results provide strong evidence that adding information-bearing features improves the forecast quality of PPMs

Evaluation of the efficiency and resulting electrical and economic losses of photovoltaic home storage systems

The increase in electricity prices along with a decrease in the price of storage systems has led to a rapid expansion of the photovoltaic (PV) home storage system market, particularly in Germany. In order to be economically viable, PV home storage systems must fulfil certain performance criteria. The overall performance and achievable self-sufficiency ratio of a PV battery home storage system depends on (i) the efficiencies of the system components, (ii) the standby consumption, (iii) the reaction time of the home storage system as well as (iv) the intelligence of the overall system control software. So far, PV home storage system still show very big differences in their performance. However, poor system performance can result in the system being no longer economic viable. Up to now, there have been only a few studies that deal with the evaluation and systematic comparison of the performance of PV home storage systems. For this paper the performance of 12 commercially available PV-battery systems has been analysed with a focus on the overall system efficiency. The efficiency of the systems is mainly influenced by the battery efficiency, power conversion efficiency and standby consumption of the different system components. Therefore, a testing and evaluation method has been developed. In this work the method as well as the results of the systems are presented. A detailed study of the influence of the effects of the individual losses on both total energy and monetary losses was carried out. It is shown that power conversion has the greatest influence on energy and monetary losses. For the systems under evaluation the monetary losses per year due to battery efficiency losses range between 2 €/a and 40 €/a. Monetary losses due to conversion losses range between 33 €/a and 137 €/a and due to standby consumption between 1 €/a and 46 €/a. The individual losses can be summed up to give a total loss, which lies between 44 €/a and 174 €/a

Comparison of Small EV Charging Station\u27s Load Forecasts and its Impact on the Operational Costs

For an energy management system (EMS) of a charging station (CS), information on future load is crucial. Existing models primarily focus on load forecasting for large charging stations. In this study, three different load forecasting models based on real data from a public CS with two charging points are developed. The models include two persistent models and one model that utilizes a machine learning algorithm. To assess the impact of forecasting accuracy on operational costs, a case study with dynamic electricity prices and a stationary battery storage is conducted. Using the load predictions, a mixed-integer linear programming problem is formulated to optimize the scheduling of the stationary battery charging

From individual Fuzzy Cognitive Maps to Agent Based Models: modelling multi-factorial and multi-stakeholder decision-making for water scarcity

Policy making for complex Social-Ecological Systems (SESs) is a multi-factorial and multi-stakeholder decision making process. Therefore, proper policy simulation in a SES should consider both the complex behavior of the system and the multi-stakeholders’ interventions into the system, which requires integrated methodological approaches. In this study, we simulate impacts of policy options on a farming community facing water scarcity in Rafsanjan, Iran, using an integrated modeling methodology combining an Agent Based Model (ABM) with Fuzzy Cognitive Mapping (FCM). First, the behavioral rules of farmers and the causal relations among environmental variables are captured with FCMs that are developed with both qualitative and quantitative data, i.e. farmers’ knowledge and empirical data from studies. Then, an ABM is developed to model decisions and actions of farmers and simulate their impacts on overall groundwater use and emigration of farmers in this case study. Finally, the impacts of different policy options are simulated and compared with a baseline scenario. The results suggest that a policy of facilitating farmers’ participation in management and control of their groundwater use leads to the highest reduction of groundwater use and would help to secure farmers’ activities in Rafsanjan. Our approach covers four main aspects that are crucial for policy simulation in SESs: 1) causal relationships, 2) feedback mechanisms, 3) social-spatial heterogeneity and 4) temporal dynamics. This approach is particularly useful for ex-ante policy options analysis