Monitoring and diagnosis of energy consumption in wastewater treatment plants. A state of the art and proposals for improvement
S Longo, BM d'Antoni, M Bongards, A Chaparro… - Applied energy, 2016 - Elsevier
In response to strong growth in energy intensive wastewater treatment, public agencies and
industry began to explore and implement measures to ensure achievement of the targets …
industry began to explore and implement measures to ensure achievement of the targets …
Review of developments in whole-building statistical energy consumption models for commercial buildings
A significant portion of energy consumption occurs in buildings today. Accurate and easy-to-
implement methods are needed to calculate building energy consumption for a wide range …
implement methods are needed to calculate building energy consumption for a wide range …
Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques
Load forecasting problems have traditionally been addressed using various statistical
methods, among which autoregressive integrated moving average with exogenous inputs …
methods, among which autoregressive integrated moving average with exogenous inputs …
Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks
This paper presents a robust short-term electrical load forecasting framework that can
capture variations in building operation, regardless of building type and location. Nine …
capture variations in building operation, regardless of building type and location. Nine …
A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables
Selection of appropriate climatic variables for prediction of electricity demand is critical as it
affects the accuracy of the prediction. Different climatic variables may have different impacts …
affects the accuracy of the prediction. Different climatic variables may have different impacts …
Big-data for building energy performance: Lessons from assembling a very large national database of building energy use
Building energy data has been used for decades to understand energy flows in buildings
and plan for future energy demand. Recent market, technology and policy drivers have …
and plan for future energy demand. Recent market, technology and policy drivers have …
[HTML][HTML] Role of input features in developing data-driven models for building thermal demand forecast
C Wang, X Li, H Li - Energy and Buildings, 2022 - Elsevier
The energy consumption of buildings accounts for a major share in the modern society.
Accurate forecast of building thermal demand is of great significance to both building …
Accurate forecast of building thermal demand is of great significance to both building …
CBLSTM-AE: a hybrid deep learning framework for predicting energy consumption
Multisource energy data, including from distributed energy resources and its multivariate
nature, necessitate the integration of robust data predictive frameworks to minimise …
nature, necessitate the integration of robust data predictive frameworks to minimise …
Accuracy of automated measurement and verification (M&V) techniques for energy savings in commercial buildings
Trustworthy savings calculations are critical to convincing investors in energy efficiency
projects of the benefit and cost-effectiveness of such investments and their ability to replace …
projects of the benefit and cost-effectiveness of such investments and their ability to replace …
A hybrid short-term load forecasting model and its application in ground source heat pump with cooling storage system
Y Xie, P Hu, N Zhu, F Lei, L Xing, L Xu, Q Sun - renewable energy, 2020 - Elsevier
This paper proposed a hybrid hour-ahead forecast model, which combines multiple
superimposed long and short term memory (LSTM) network and back-propagation neural …
superimposed long and short term memory (LSTM) network and back-propagation neural …