scholarly journals Expect: EXplainable Prediction Model for Energy ConsumpTion

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 248
Author(s):  
Amira Mouakher ◽  
Wissem Inoubli ◽  
Chahinez Ounoughi ◽  
Andrea Ko
Keyword(s):  
Energy Consumption ◽  
Prediction Models ◽  
Resource Depletion ◽  
Sustainable Cities ◽  
Energy Prediction ◽  
Building Trust ◽  
Energy Demands ◽  
Steady Growth ◽  
World Energy ◽  
Deep Learning Model

With the steady growth of energy demands and resource depletion in today’s world, energy prediction models have gained more and more attention recently. Reducing energy consumption and carbon footprint are critical factors for achieving efficiency in sustainable cities. Unfortunately, traditional energy prediction models focus only on prediction performance. However, explainable models are essential to building trust and engaging users to accept AI-based systems. In this paper, we propose an explainable deep learning model, called Expect, to forecast energy consumption from time series effectively. Our results demonstrate our proposal’s robustness and accuracy when compared to the baseline methods.

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.

scholarly journals Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1288 ◽  
Author(s):  
Philip Shine ◽  
John Upton ◽  
Paria Sefeedpari ◽  
Michael D. Murphy
Keyword(s):  
Energy Consumption ◽  
Environmental Sustainability ◽  
Prediction Models ◽  
Farming Systems ◽  
Primary Energy ◽  
Dairy Farming ◽  
Machine Learning Algorithms ◽  
Energy Prediction ◽  
Prediction Modelling ◽  
The Impact

The global consumption of dairy produce is forecasted to increase by 19% per person by 2050. However, milk production is an intense energy consuming process. Coupled with concerns related to global greenhouse gas emissions from agriculture, increasing the production of milk must be met with the sustainable use of energy resources, to ensure the future monetary and environmental sustainability of the dairy industry. This body of work focused on summarizing and reviewing dairy energy research from the monitoring, prediction modelling and analyses point of view. Total primary energy consumption values in literature ranged from 2.7 MJ kg−1 Energy Corrected Milk on organic dairy farming systems to 4.2 MJ kg−1 Energy Corrected Milk on conventional dairy farming systems. Variances in total primary energy requirements were further assessed according to whether confinement or pasture-based systems were employed. Overall, a 35% energy reduction was seen across literature due to employing a pasture-based dairy system. Compared to standard regression methods, increased prediction accuracy has been demonstrated in energy literature due to employing various machine-learning algorithms. Dairy energy prediction models have been frequently utilized throughout literature to conduct dairy energy analyses, for estimating the impact of changes to infrastructural equipment and managerial practices.

Ensemble Neural Network Model for Predicting the Energy Consumption of a Milling Machine

2015 ◽  
Author(s):  
Ronay Ak ◽  
Moneer M. Helu ◽  
Sudarsan Rachuri
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Prediction Models ◽  
Production Systems ◽  
Face Milling ◽  
Model Parameters ◽  
Efficient Production ◽  
Milling Machine ◽  
Energy Prediction

Accurate prediction of the energy consumption is critical for energy-efficient production systems. However, the majority of existing prediction models aim at providing only point predictions and can be affected by uncertainties in the model parameters and input data. In this paper, a prediction model that generates prediction intervals (PIs) for estimating energy consumption of a milling machine is proposed. PIs are used to provide information on the confidence in the prediction by accounting for the uncertainty in both the model parameters and the noise in the input variables. An ensemble model of neural networks (NNs) is used to estimate PIs. A k-nearest-neighbors (k-nn) approach is applied to identify similar patterns between training and testing sets to increase the accuracy of the results by using local information from the closest patterns of the training sets. Finally, a case study that uses a dataset obtained by machining 18 parts through face-milling, contouring, slotting and pocketing, spiraling, and drilling operations is presented. Of these six operations, the case study focuses on face milling to demonstrate the effectiveness of the proposed energy prediction model.

scholarly journals A Solar Altitude Angle Model for Efficient Solar Energy Predictions

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1391 ◽  
Author(s):  
Sergio Herrería-Alonso ◽  
Andrés Suárez-González ◽  
Miguel Rodríguez-Pérez ◽  
Raúl F. Rodríguez-Rubio ◽  
Cándido López-García
Keyword(s):  
Energy Harvesting ◽  
Solar Energy ◽  
Prediction Models ◽  
Low Complexity ◽  
Time Of Day ◽  
Solar Altitude ◽  
Energy Prediction ◽  
Energy Demands ◽  
Solar Powered ◽  
Set Up

Sunlight is one of the most frequently used ambient energy sources for energy harvesting in wireless sensor networks. Although virtually unlimited, solar radiation experiences significant variations depending on the weather, the season, and the time of day, so solar-powered nodes commonly employ solar prediction models to effectively adapt their energy demands to harvesting dynamics. We present in this paper a novel energy prediction model that makes use of the altitude angle of the sun at different times of day to predict future solar energy availability. Unlike most of the state-of-the-art predictors that use past energy observations to make predictions, our model does not require one to maintain local energy harvesting patterns of past days. Performance evaluation shows that our scheme is able to provide accurate predictions for arbitrary forecasting horizons by performing just a few low complexity operations. Moreover, our proposal is extremely simple to set up since it does not require any particular tuning for each different scenario or location.

scholarly journals Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

2020 ◽  
Vol 12 (4) ◽  
pp. 1417 ◽  
Author(s):  
Andreea Valeria Vesa ◽  
Tudor Cioara ◽  
Ionut Anghel ◽  
Marcel Antal ◽  
Claudia Pop ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Demand Response ◽  
Prediction Error ◽  
Energy Demand ◽  
Business Processes ◽  
Prediction Models ◽  
Percentage Error ◽  
Energy Prediction ◽  
Demand Prediction ◽  
Optimal Ensemble

In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs. For DCs’ successful participation in such programs and for minimizing the risks for their core business processes, their energy demand and potential flexibility must be accurately forecasted in advance. Therefore, in this paper, we propose an energy prediction model that uses a genetic heuristic to determine the optimal ensemble of a set of neural network prediction models to minimize the prediction error and the uncertainty concerning DR participation. The model considers short term time horizons (i.e., day-ahead and 4-h-ahead refinements) and different aspects such as the energy demand and potential energy flexibility (the latter being defined in relation with the baseline energy consumption). The obtained results, considering the hardware characteristics as well as the historical energy consumption data of a medium scale DC, show that the genetic-based heuristic improves the energy demand prediction accuracy while the intra-day prediction refinements further reduce the day-ahead prediction error. In relation to flexibility, the prediction of both above and below baseline energy flexibility curves provides good results for the mean absolute percentage error (MAPE), which is just above 6%, allowing for safe DC participation in DR programs.

scholarly journals Toward a Generalized Energy Prediction Model for Machine Tools

2016 ◽  
Vol 139 (4) ◽  
Author(s):  
Raunak Bhinge ◽  
Jinkyoo Park ◽  
Kincho H. Law ◽  
David A. Dornfeld ◽  
Moneer Helu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Machine Tool ◽  
Process Planning ◽  
Machine Tools ◽  
Prediction Models ◽  
Machining Process ◽  
Data Driven ◽  
Process Data ◽  
Energy Prediction

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian process (GP) regression, a nonparametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed by any part of the machine using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

A Generalized Data-Driven Energy Prediction Model With Uncertainty for a Milling Machine Tool Using Gaussian Process

2015 ◽  
Author(s):  
Jinkyoo Park ◽  
Kincho H. Law ◽  
Raunak Bhinge ◽  
Nishant Biswas ◽  
Amrita Srinivasan ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Machine Tool ◽  
Gaussian Process ◽  
Prediction Models ◽  
Numerical Control ◽  
Data Driven ◽  
Machining Parameters ◽  
Milling Machine ◽  
Energy Prediction

Using a machine learning approach, this study investigates the effects of machining parameters on the energy consumption of a milling machine tool, which would allow selection of optimal operational strategies to machine a part with minimum energy. Data-driven prediction models, built upon a nonlinear regression approach, can be used to gain an understanding of the effects of machining parameters on energy consumption. In this study, we use the Gaussian Process to construct the energy prediction model for a computer numerical control (CNC) milling machine tool. Energy prediction models for different machining operations are constructed based on collected data. With the collected data sets, optimum input features for model selection are identified. We demonstrate how the energy prediction models can be used to compare the energy consumption for the different operations and to estimate the total energy usage for machining a generic part. We also present an uncertainty analysis to develop confidence bounds for the prediction model and to provide insight into the vast parameter space and training required to improve the accuracy of the model. Generic parts are machined to test and validate the prediction model constructed using the Gaussian Process and we consistently achieve an accuracy of over 95 % on the total predicted energy.

scholarly journals A Novel Load Forecasting Approach Based on Smart Meter Data Using Advance Preprocessing and Hybrid Deep Learning

2021 ◽  
Vol 11 (6) ◽  
pp. 2742
Author(s):  
Fatih Ünal ◽  
Abdulaziz Almalaq ◽  
Sami Ekici
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Prediction Models ◽  
Short Term Memory ◽  
Critical Role ◽  
Load Forecasting ◽  
Unified Framework ◽  
Short Term ◽  
Planning And Scheduling ◽  
Short Term Load Forecasting

Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the customer-level, thanks to smart meters, is a complicated problem due to the high variability and uncertainty of load consumptions as well as customer privacy issues. In terms of customers’ short-term load forecasting, these models include a high level of nonlinearity between input data and output predictions, demanding more robustness, higher prediction accuracy, and generalizability. In this paper, we develop an advanced preprocessing technique coupled with a hybrid sequential learning-based energy forecasting model that employs a convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) within a unified framework for accurate energy consumption prediction. The energy consumption outliers and feature clustering are extracted at the advanced preprocessing stage. The novel hybrid deep learning approach based on data features coding and decoding is implemented in the prediction stage. The proposed approach is tested and validated using real-world datasets in Turkey, and the results outperformed the traditional prediction models compared in this paper.

scholarly journals The Impact Assessment of Climate Change on Building Energy Consumption in Poland

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4084
Author(s):  
Hassan Bazazzadeh ◽  
Peiman Pilechiha ◽  
Adam Nadolny ◽  
Mohammadjavad Mahdavinejad ◽  
Seyedeh sara Hashemi safaei
Keyword(s):  
Climate Change ◽  
Energy Consumption ◽  
Thermal Load ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Building Sector ◽  
Energy Demands ◽  
Heating Load ◽  
The Impact ◽  
The City

A substantial share of the building sector in global energy demand has attracted scholars to focus on the energy efficiency of the building sector. The building’s energy consumption has been projected to increase due to mass urbanization, high living comfort standards, and, more importantly, climate change. While climate change has potential impacts on the rate of energy consumption in buildings, several studies have shown that these impacts differ from one region to another. In response, this paper aimed to investigate the impact of climate change on the heating and cooling energy demands of buildings as influential variables in building energy consumption in the city of Poznan, Poland. In this sense, through the statistical downscaling method and considering the most recent Typical Meteorological Year (2004–2018) as the baseline, the future weather data for 2050 and 2080 of the city of Poznan were produced according to the HadCM3 and A2 GHG scenario. These generated files were then used to simulate the energy demands in 16 building prototypes of the ASHRAE 90.1 standard. The results indicate an average increase in cooling load and a decrease in heating load at 135% and 40% , respectively, by 2080. Due to the higher share of heating load, the total thermal load of the buildings decreased within the study period. Therefore, while the total thermal load is currently under the decrease, to avoid its rise in the future, serious measures should be taken to control the increased cooling demand and, consequently, thermal load and GHG emissions.

scholarly journals Forecasting Energy Consumption of Wastewater Treatment Plants with a Transfer Learning Approach for Sustainable Cities

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1149
Author(s):  
Pedro Oliveira ◽  
Bruno Fernandes ◽  
Cesar Analide ◽  
Paulo Novais
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Transfer Learning ◽  
Short Term Memory ◽  
Wastewater Treatment Plants ◽  
Learning Approach ◽  
Sustainable Cities ◽  
Short Term ◽  
Long Short Term Memory ◽  
Gated Recurrent Units

A major challenge of today’s society is to make large urban centres more sustainable. Improving the energy efficiency of the various infrastructures that make up cities is one aspect being considered when improving their sustainability, with Wastewater Treatment Plants (WWTPs) being one of them. Consequently, this study aims to conceive, tune, and evaluate a set of candidate deep learning models with the goal being to forecast the energy consumption of a WWTP, following a recursive multi-step approach. Three distinct types of models were experimented, in particular, Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), and uni-dimensional Convolutional Neural Networks (CNNs). Uni- and multi-variate settings were evaluated, as well as different methods for handling outliers. Promising forecasting results were obtained by CNN-based models, being this difference statistically significant when compared to LSTMs and GRUs, with the best model presenting an approximate overall error of 630 kWh when on a multi-variate setting. Finally, to overcome the problem of data scarcity in WWTPs, transfer learning processes were implemented, with promising results being achieved when using a pre-trained uni-variate CNN model, with the overall error reducing to 325 kWh.

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