Study on the Impact of Building Energy Predictions Considering Weather Errors of Neighboring Weather Stations
Weather data errors affect energy management by influencing the accuracy of building energy predictions. This study presents a long short-term memory (LSTM) prediction model based on the "Energy Detective" dataset (Shanghai, China) and neighboring weather station data. The study analyzes the errors of different weather data sources (Detective and A) at the same latitude and longitude. Subsequently, it discusses the effects of weather errors from neighboring weather stations (Detective, A, B, C, and D) on energy forecasts for the next hour and day including the selection process for neighboring weather stations. Furthermore, it compares the forecast results for summer and autumn. The findings indicate a correlation between weather errors from neighboring weather stations and energy consumption. The median R-Square for predicting the next hour reached 0.95. The model's predictions for the next day exhibit a higher Prediction Interval Mean Width (139.0 in summer and 146.1 in autumn), indicating a greater uncertainty.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
|---|---|
| Enthalten in: |
Sensors (Basel, Switzerland) - 24(2024), 4 vom: 09. Feb. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Li, Guannan [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Building energy predictions |
|---|
| Anmerkungen: |
Date Revised 27.02.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.3390/s24041157 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM368902943 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM368902943 | ||
| 003 | DE-627 | ||
| 005 | 20240229164819.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240229s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.3390/s24041157 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1309.xml |
| 035 | |a (DE-627)NLM368902943 | ||
| 035 | |a (NLM)38400316 | ||
| 035 | |a (PII)1157 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Li, Guannan |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Study on the Impact of Building Energy Predictions Considering Weather Errors of Neighboring Weather Stations |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 27.02.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Weather data errors affect energy management by influencing the accuracy of building energy predictions. This study presents a long short-term memory (LSTM) prediction model based on the "Energy Detective" dataset (Shanghai, China) and neighboring weather station data. The study analyzes the errors of different weather data sources (Detective and A) at the same latitude and longitude. Subsequently, it discusses the effects of weather errors from neighboring weather stations (Detective, A, B, C, and D) on energy forecasts for the next hour and day including the selection process for neighboring weather stations. Furthermore, it compares the forecast results for summer and autumn. The findings indicate a correlation between weather errors from neighboring weather stations and energy consumption. The median R-Square for predicting the next hour reached 0.95. The model's predictions for the next day exhibit a higher Prediction Interval Mean Width (139.0 in summer and 146.1 in autumn), indicating a greater uncertainty | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a building energy predictions | |
| 650 | 4 | |a long short-term memory (LSTM) | |
| 650 | 4 | |a predicting next 1 h vs. predicting next 1 day | |
| 650 | 4 | |a weather errors at neighboring weather stations | |
| 700 | 1 | |a Wang, Yong |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Chunzhi |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Chengliang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhan, Lei |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Sensors (Basel, Switzerland) |d 2007 |g 24(2024), 4 vom: 09. Feb. |w (DE-627)NLM187985170 |x 1424-8220 |7 nnns |
| 773 | 1 | 8 | |g volume:24 |g year:2024 |g number:4 |g day:09 |g month:02 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.3390/s24041157 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 24 |j 2024 |e 4 |b 09 |c 02 | ||