Dynamic whole system testing of combined renewable heating systems : the current state of the art

Objective: For the evaluation of the energetic performance of combined renewable heating systems that supply space heat and domestic hot water for single family houses, dynamic behaviour, component interactions, and control of the system play a crucial role and should be included in test methods. Methods: New dynamic whole system test methods were developed based on “hardware in the loop” concepts. Three similar approaches are described and their differences are discussed. The methods were applied for testing solar thermal systems in combination with fossil fuel boilers (heating oil and natural gas), biomass boilers, and/or heat pumps. Results: All three methods were able to show the performance of combined heating systems under transient operating conditions. The methods often detected unexpected behaviour of the tested system that cannot be detected based on steady state performance tests that are usually applied to single components. Conclusion: Further work will be needed to harmonize the different test methods in order to reach comparable results between the different laboratories. Practice implications: A harmonized approach for whole system tests may lead to new test standards and improve the accuracy of performance prediction as well as reduce the need for field tests.

Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.