| 1. |
- Olofsson, Thomas, 1968-, et al.
(författare)
-
A method for predicting the annual building heating demand based on limited performance data
- 1998
-
Ingår i: Energy and Buildings. - : Elsevier. - 0378-7788 .- 1872-6178. ; 28:1, s. 101-108
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present an investigation of the possibility to use a neural network combined with a quasi-physical description in order to predict the annual supplied space heating demand (P) for a number of small single family buildings located in the North of Sweden. As a quasi-physical description for P, we used measured diurnal performance data from a similar building or simulated data from a steady state energy simulation software. We show that the required supplied space heating demand may be predicted with an average accuracy of 5%. The predictions were based on access to measured diurnal data of indoor and outdoor temperatures and the supplied heating demand from a limited time period, ranging from 10 to 35 days. The prediction accuracy was found to be almost independent of what time of the year the measurements were obtained from, except for periods when the supplied heating demand was very small. For models based on measurements from May and fo some buildings from April and September, the prediction was less accurate.
|
|
| 2. |
- Olofsson, Thomas, 1968-, et al.
(författare)
-
Energy load predictions for buildings based on a total demand perspective
- 1998
-
Ingår i: Energy and Buildings. - : Elsevier. - 0378-7788 .- 1872-6178. ; 28:1, s. 109-116
-
Tidskriftsartikel (refereegranskat)abstract
- The outline of this work was to develop models for single family buildings, based on a total energy demand perspective, i.e., building-climate-inhabitants. The building-climate part was included by using a commercial dynamic energy simulation software. Whereas the influence from the inhabitants was implemented in terms of a predicted load for domestic equipment and hot water preparation, based on a reference building. The estimations were processed with neural network techniques. All models were based on access to measured diurnal data from a limited time period, ranging from 10 to 35 days. The annual energy predictions were found to be improved, compared to models based on only a building-climate perspective, when the domestic load was included. For periods with a small heating demand, i.e., May-September, the average accuracy was 7% and 4% for the heating and total energy load, respectively, whereas for the rest of the year the accuracy was on average 3% for both heating and total energy load.
|
|
