Data driven prediction models of energy use of appliances in a low-energy house

2017 ◽  
Vol 140 ◽  
pp. 81-97 ◽  
Author(s):  
Luis M. Candanedo ◽  
Véronique Feldheim ◽  
Dominique Deramaix
Keyword(s):  
Energy Use ◽  
Prediction Models ◽  
Low Energy ◽  
Data Driven

scholarly journals Forecasting COVID-19 Dynamics and Endpoint in Bangladesh: A Data-driven Approach

2020 ◽  
Author(s):  
Al-Ekram Elahee Hridoy ◽  
Mohammad Naim ◽  
Nazim Uddin Emon ◽  
Imrul Hasan Tipo ◽  
Safayet Alam ◽  
...  
Keyword(s):  
Social Life ◽  
Inflection Point ◽  
Prediction Models ◽  
Short Term Memory ◽  
Data Driven ◽  
World Health ◽  
Estimation Methods ◽  
Atypical Pneumonia ◽  
List Type ◽  
Logistic Curve

AbstractOn December 31, 2019, the World Health Organization (WHO) was informed that atypical pneumonia-like cases have emerged in Wuhan City, Hubei province, China. WHO identified it as a novel coronavirus and declared a global pandemic on March 11th, 2020. At the time of writing this, the COVID-19 claimed more than 440 thousand lives worldwide and led to the global economy and social life into an abyss edge in the living memory. As of now, the confirmed cases in Bangladesh have surpassed 100 thousand and more than 1343 deaths putting startling concern on the policymakers and health professionals; thus, prediction models are necessary to forecast a possible number of cases in the future. To shed light on it, in this paper, we presented data-driven estimation methods, the Long Short-Term Memory (LSTM) networks, and Logistic Curve methods to predict the possible number of COVID-19 cases in Bangladesh for the upcoming months. The results using Logistic Curve suggests that Bangladesh has passed the inflection point on around 28-30 May 2020, a plausible end date to be on the 2nd of January 2021 and it is expected that the total number of infected people to be between 187 thousand to 193 thousand with the assumption that stringent policies are in place. The logistic curve also suggested that Bangladesh would reach peak COVID-19 cases at the end of August with more than 185 thousand total confirmed cases, and around 6000 thousand daily new cases may observe. Our findings recommend that the containment strategies should immediately implement to reduce transmission and epidemic rate of COVID-19 in upcoming days.HighlightsAccording to the Logistic curve fitting analysis, the inflection point of the COVID-19 pandemic has recently passed, which was approximately between May 28, 2020, to May 30, 2020.It is estimated that the total number of confirmed cases will be around 187-193 thousand at the end of the epidemic. We expect that the actual number will most likely to in between these two values, under the assumption that the current transmission is stable and improved stringent policies will be in place to contain the spread of COVID-19.The estimated total death toll will be around 3600-4000 at the end of the epidemic.The epidemic of COVID-19 in Bangladesh will be mostly under control by the 2nd of January 2021 if stringent measures are taken immediately.

Integrating Rule-Based Systems and Data Analytics Tools Using Open Standard PMML

2015 ◽  
Author(s):  
Yunpeng Li ◽  
Utpal Roy ◽  
Y. Tina Lee ◽  
Sudarsan Rachuri
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Data Analytics ◽  
Domain Knowledge ◽  
Prediction Models ◽  
Data Driven ◽  
Sensor Data ◽  
Formal Representation ◽  
Rule Based ◽  
Open Standard

Rule-based expert systems such as CLIPS (C Language Integrated Production System) are 1) based on inductive (if-then) rules to elicit domain knowledge and 2) designed to reason new knowledge based on existing knowledge and given inputs. Recently, data mining techniques have been advocated for discovering knowledge from massive historical or real-time sensor data. Combining top-down expert-driven rule models with bottom-up data-driven prediction models facilitates enrichment and improvement of the predefined knowledge in an expert system with data-driven insights. However, combining is possible only if there is a common and formal representation of these models so that they are capable of being exchanged, reused, and orchestrated among different authoring tools. This paper investigates the open standard PMML (Predictive Model Mockup Language) in integrating rule-based expert systems with data analytics tools, so that a decision maker would have access to powerful tools in dealing with both reasoning-intensive tasks and data-intensive tasks. We present a process planning use case in the manufacturing domain, which is originally implemented as a CLIPS-based expert system. Different paradigms in interpreting expert system facts and rules as PMML models (and vice versa), as well as challenges in representing and composing these models, have been explored. They will be discussed in detail.

Data driven occupancy information for energy simulation and energy use assessment in residential buildings

Energy ◽  
2021 ◽  
Vol 218 ◽  
pp. 119539
Author(s):  
Karthik Panchabikesan ◽  
Fariborz Haghighat ◽  
Mohamed El Mankibi
Keyword(s):  
Energy Use ◽  
Residential Buildings ◽  
Energy Simulation ◽  
Data Driven

scholarly journals Dynamic thermal response of low-energy residential buildings based on in-wall measurements

2019 ◽  
Vol 111 ◽  
pp. 04002 ◽  
Author(s):  
Kyriaki Foteinaki ◽  
Rongling Li ◽  
Alfred Heller ◽  
Morten Herget Christensen ◽  
Carsten Rode
Keyword(s):  
Air Temperature ◽  
Energy Use ◽  
Residential Buildings ◽  
Measurement Data ◽  
Thermal Response ◽  
Low Energy ◽  
Space Heating ◽  
Internal Wall ◽  
Load Bearing ◽  
Concrete Elements

This study analysed the dynamic thermal response of a low-energy building using measurement data from an apartment block in Copenhagen, Denmark. Measurements were collected during February and July 2018 on space heating energy use, set-points, room air temperature and temperature from sensors integrated inside concrete elements, i.e. internal walls and ceiling, at different heights and depths. The heating system was controlled by the occupants. During February, there were unusually high set-points for some days and a regular heating pattern for some other days. Overheating was observed during July. A considerable effect of solar gain was observed both during winter and summer months. The room air temperature fluctuations were observed at a certain extent inside the concrete elements; higher in the non-load-bearing internal wall, followed by the load-bearing internal wall and lastly by the ceiling. The phenomenon of delayed thermal response of the concrete elements was observed. All internal concrete masses examined may be regarded as active elements and can contribute to the physically available heat storage potential of the building. The study provides deep insight into the thermal response of concrete elements in low-energy residential buildings, which should be considered when planning a flexible space heating energy use.

Building Energy Prediction Using Artificial Neural Networks (LSTM)

2020 ◽  
Author(s):  
Sankhanil Goswami
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Energy Use ◽  
Prediction Models ◽  
Short Term Memory ◽  
Significant Proportion ◽  
Cooling Load ◽  
Energy Prediction ◽  
Artificial Neural

Abstract Modern buildings account for a significant proportion of global energy consumption worldwide. Therefore, accurate energy use forecast is necessary for energy management and conservation. With the advent of smart sensors, a large amount of accurate energy data is available. Also, with the advancements in data analytics and machine learning, there have been numerous studies on developing data-driven prediction models based on Artificial Neural Networks (ANNs). In this work a type of ANN called Large Short-Term Memory (LSTM) is used to predict the energy use and cooling load of an existing building. A university administrative building was chosen for its typical commercial environment. The network was trained with one year of data and was used to predict the energy consumption and cooling load of the following year. The mean absolute testing error for the energy consumption and the cooling load were 0.105 and 0.05. The percentage mean accuracy was found to be 92.8% and 96.1%. The process was applied to several other buildings in the university and similar results were obtained. This indicates the model can successfully predict the energy consumption and cooling load for the buildings studied. The further improvement and application of this technique for optimizing building performance are also explored.

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