Forecasting Energy Demand & Peak Load Days with the Inclusion of Solar Energy Production

The addition of solar panels to forecasting energy demand and peak energy demand presents an entirely new challenge to a facility. By having to account for the varying energy generation from the solar panels on any given day based on the weather it becomes increasingly difficult to accurately predict energy demand. With renewable energy sources becoming more prevalent, new methods to track peak energy demand are needed to account for the energy provided by renewable sources. We know from previous research that Artificial Neural Networks (ANN) and Auto Regressive Integrated Moving Average (ARIMA) models are both capable of accurately forecasting building demand and peak electric load days without the presence of solar panels. The goal of this research was to take three different approaches for both the ANN model and the ARIMA model to find the most accurate method for forecasting monthly energy demand and peak load days while considering the varying daily solar energy production. The first approach used was to forecast net demand outright based on relevant historical training data including weather information that would help the models learn how this information affected the overall net demand. The second approach was to forecast the building demand specifically based on the same relevant historical data and then use a random decision tree forest to predict the cluster of day that each day of the month would be in terms of solar production (high, medium with early peak, medium with late peak, low). After the type of day was predicted we would subtract the average solar energy production of the predicted cluster to receive our forecasted net demand for that day. The third approach was similar to the second, but instead of subtracting the average of the cluster we subtracted multiple randomly generated days from that cluster to provide multiple overlapping forecasts. This was specifically used to try and better predict peak load days by testing the hypothesis that if 80% or higher predicted a peak day it would in fact be a peak day. The ANN model outperformed the ARIMA for each approach. Forecasting multiple days was the best of the three approaches. The multiple day ANN forecast had the highest balanced accuracy and sensitivity, the net demand ANN approach was the 2nd most accurate approach and the average solar ANN forecast was the 3rd best approach in terms of balanced accuracy and sensitivity. Based on the outcomes of this study, consumers and institutions such as RIT will be better able to predict peak usage days and use preventative measures to save money by reducing their energy intake on those predicted days. Another benefit will be that energy distribution companies will be able to accurately predict the amount of energy customers with personal solar panels will need in addition to the solar energy they are using. This will allow a greater level of reliability from the providers. Being able to accurately forecast energy demand with the presence of solar energy is going to be critical with the ever-increasing usage of renewable energy

Forecasting Strategies for Predicting Peak Electric Load Days

Academic institutions spend thousands of dollars every month on their electric power consumption. Some of these institutions follow a demand charges pricing structure; here the amount a customer pays to the utility is decided based on the total energy consumed during the month, with an additional charge based on the highest average power load required by the customer over a moving window of time as decided by the utility. Therefore, it is crucial for these institutions to minimize the time periods where a high amount of electric load is demanded over a short duration of time. In order to reduce the peak loads and have more uniform energy consumption, it is imperative to predict when these peaks occur, so that appropriate mitigation strategies can be developed. The research work presented in this thesis has been conducted for Rochester Institute of Technology (RIT), where the demand charges are decided based on a 15 minute sliding window panned over the entire month. This case study makes use of different statistical and machine learning algorithms to develop a forecasting strategy for predicting the peak electric load days of the month. The proposed strategy was tested for a whole year starting May 2015 to April 2016 during which a total of 57 peak days were observed. The model predicted a total of 74 peak days during this period, 40 of these cases were true positives, hence achieving an accuracy level of 70 percent. The results obtained with the proposed forecasting strategy are promising and demonstrate an annual savings potential worth about $80,000 for a single submeter of RIT

Forecasting peak energy demand for smart buildings

Predicting energy consumption in buildings plays an important part in the process of digital transformation of the built environment, and for understanding the potential for energy savings. This also contributes to reducing the impact of climate change, where buildings need to increase their adaptability and resilience while reducing energy consumption and maintain user comfort. The use of Internet of Things devices for monitoring and control of energy consumption in buildings can take into account user preferences, event monitoring and building optimization. Detecting peak energy demand from historical building data can enable users to manage their energy use more efficiently, while also enabling real-time response strategies (including control and actuation) to known or future scenarios. Several statistical, time series, and machine learning techniques are proposed in this work to predict electricity consumption for five different building types, by using peak demand forecasting to achieve energy efficiency. We have used several indigenous and exogenous variables with a view to test different energy forecasting scenarios. The suggested techniques are evaluated for creating predictive models, including linear Regression, dynamic regression, ARIMA time series, exponential smoothing time series, artificial neural network, and deep neural network. We conduct the analysis on an energy consumption dataset of five buildings from 2014 until 2019. Our results show that for a day ahead prediction, the ARIMA model outperforms the other approaches with an accuracy of 98.91% when executed over a 168 h (1 week) of uninterrupted data for five government buildings

On the surplus accuracy of data-driven energy quantification methods in the residential sector

Increasing trust in energy performance certificates (EPCs) and drawing meaningful conclusions requires a robust and accurate determination of building energy performance (BEP). However, existing and by law prescribed engineering methods, relying on physical principles, are under debate for being error-prone in practice and ultimately inaccurate. Research has heralded data-driven methods, mostly machine learning algorithms, to be promising alternatives: various studies compare engineering and data-driven methods with a clear advantage for data-driven methods in terms of prediction accuracy for BEP. While previous studies only investigated the prediction accuracy for BEP, it yet remains unclear which reasons and cause–effect relationships lead to the surplus prediction accuracy of data-driven methods. In this study, we develop and discuss a theory on how data collection, the type of auditor, the energy quantification method, and its accuracy relate to one another. First, we introduce cause–effect relationships for quantifying BEP method-agnostically and investigate the influence of several design parameters, such as the expertise of the auditor issuing the EPC, to develop our theory. Second, we evaluate and discuss our theory with literature. We find that data-driven methods positively influence cause–effect relationships, compensating for deficits due to auditors’ lack of expertise, leading to high prediction accuracy. We provide recommendations for future research and practice to enable the informed use of data-driven methods

A data-driven multi-regime approach for predicting real-time energy consumption of industrial machines.

This thesis focuses on methods for improving energy consumption prediction performance in complex industrial machines. Working with real-world industrial machines brings several challenges, including data access, algorithmic bias, data privacy, and the interpretation of machine learning algorithms. To effectively manage energy consumption in the industrial sector, it is essential to develop a framework that enhances prediction performance, reduces energy costs, and mitigates air pollution in heavy industrial machine operations. This study aims to assist managers in making informed decisions and driving the transition towards green manufacturing. The energy consumption of industrial machinery is substantial, and the recent increase in CO2 emissions is a major concern. Consequently, energy efficiency is becoming gradually more crucial for businesses, governments, and the environment. Accurately estimating the power consumption of industrial machines might be useful in adjusting machine operation settings for operators. Smart devices can help manage electricity consumption more efficiently by providing necessary data that can be used to make better decisions. However, datasets from real-world industrial machines often contain several challenges, including unexpected changes over time, inconsistent operating conditions, missing data, unknown working environments, unrecorded maintenance, and human errors. The utilization of energy consumption patterns can enable more accurate calculation methods. Precisely predicting the energy consumption of heavy industrial machines is fundamental for enhancing energy efficiency and minimizing blackouts. While numerous research papers have concentrated on improving the prediction accuracy of traditional static machine learning models, these models may not perform well as data evolves by time. The presence of expected or unexpected variations, known as concept drift, can have a significant impact on the performance of machine learning models over time. Therefore, it is crucial to design a method that takes these potential changes into account when predicting power consumption for industrial machines. A novel data-driven dynamic modeling approach was developed in this dissertation to detect repetitive machine running regimes and improve the prediction accuracy of energy usage for industrial machines. All designed methods, including traditional static modeling, were applied to three distinct real-world industrial machine datasets. The experimental results obtained from these implementations are thoroughly discussed and presented in detail. In the first part of the dissertation, the proposed multi-regime approach successfully detects repeated machine multi-regime running conditions and maintains a better overall prediction performance over time compared to other methods. Secondly, the designed dynamic method for predicting energy consumption of industrial machines under repetitive regimes outperforms traditional static modeling and ensemble modeling in real-time prediction. Lastly, the implemented statistical tests demonstrated that the proposed dynamic method achieved a significant improvement in prediction performance accuracy compared to the other applied methods. According to the experimental results, the developed dynamic modeling proves to be applicable to various industrial machines with complex structures and features, delivering a more precise prediction performance. As a result of this study, companies can gain a better understanding of their machine working conditions and make necessary adaptations to decrease their energy consumption over time. Overall, this dissertation emphasizes the importance of industrial machine data in the energy sector and the potential for data-driven solutions to enhance energy efficiency in industrial machinery. The ultimate goal is to encourage further research in energy efficiency with the aim of reducing air pollution and facilitating a timely transition towards green manufacturing

Short-term forecasting for the electrical demand of Heating, Ventilation, and Air Conditioning systems

The heating, ventilation, and air conditioning systems (HVAC) of large scale commercial and institutional buildings can have significant contributions to the buildings overall electric demand. During periods of peak demand, utilities are faced with a challenge of balancing supply and demand while the system is under stress. As such, utility companies began to operate demand response programs for large scale consumers. Participation in such programs requires the participant to shift their electric demand to off-peak hours in exchange for monetary compensation. In such a context, it is beneficial for large scale commercial and institutional buildings to participate in such programs. In order to effectively plan demand response based strategies, building energy managers and operators require accurate tools for the short-term forecasting of large scale components and systems within the building. This thesis contributes to the field of demand response research by proposing a method for the short-term forecasting for the electric demand of an HVAC system in an institutional building. Two machine learning based approaches are proposed in this work: a component method and a system based method. The component-level approach forecasts the electric demand of a component within the HVAC system (e.g. air supply fans) using an autoregressive neural network coupled with a physics based equation. The system-level approach uses deep learning models to forecast the overall electric demand of the HVAC system through forecasting the electric demand of the primary and secondary system. Both approaches leverage available data from the building automation system (BAS) without the need for additional sensors. The system based forecasting method is validated through a case study for a single building with two data sources: measurement data obtained from the BAS and from an eQuest simulation of the building. The building used as the case study for the work herein consists of the Genomic building of Concordia University Loyola campus

Machine learning for human-centered and value-sensitive building energy efficiency

Enhancing building energy efficiency is one of the best strategies to reduce energy consumption and associated CO2 emissions. Recent studies emphasized the importance of occupant behavior as a key means of enhancing building energy efficiency. However, it is also critical that while we strive to enhance the energy efficiency of buildings through improving occupant behavior, we still pay enough attention to occupant comfort and satisfaction. Towards this goal, this research proposes a data-driven machine-learning-based approach to behavioral building energy efficiency, which could help better understand and predict the impact of occupant behavior on building energy consumption and occupant comfort; and help optimize occupant behavior for both energy saving and occupant comfort. Three types of models were developed and tested – simulation-data-driven, real-data-driven, and hybrid. Accordingly, the research included five primary research tasks. First, the importance levels of energy-related human values (e.g., thermal comfort) to building occupants and their current satisfaction levels with these values were identified, in order to better understand the factors that are associated with higher/lower importance and/or satisfaction levels and identify the potential factors that could help predict occupant comfort. Second, a data sensing and occupant feedback collection plan was developed, in order to capture and monitor the indoor environmental conditions, energy consumption, energy-related occupant behavior, and occupant comfort in real buildings. Third, a set of buildings were simulated, in order to model the energy consumption of different buildings in different contexts – in terms of occupant behavior, building sizes, weather conditions, etc.; and a simulation-data-driven occupant-behavior-sensitive machine learning-based model, which learns from simulation data, was developed for predicting hourly cooling energy consumption. Fourth, a set of real-data-driven occupant-behavior-sensitive machine learning-based models, which learn from real data (data collected from real buildings and real occupants), were developed for predicting hourly cooling and lighting energy consumption and thermal and visual occupant comfort; and a genetic algorithm-based optimization model for determining the optimal occupant behavior that can simultaneously reduce energy consumption and improve occupant comfort was developed. Compared to the simulation-data-driven approach, the real-data-driven approach aims to better capture and model the real-life behavior and comfort of occupants and the real-life energy-consumption patterns of buildings. Although successful in this regard, the resulting models may not generalize well outside of their training range. Fifth, a hybrid, occupant-behavior-sensitive machine learning-based model, which learns from both simulation data and real data, was developed for predicting hourly cooling and lighting energy consumption. The hybrid approach aims to overcome the limitations of both simulation-data-driven and real-data-driven approaches – especially the limited ability to capture occupant behavior and real-life consumption patterns in simulation-data-driven approaches and the limited generalizability of real-data-driven approaches to different cases – by learning from both types of data simultaneously. The experimental results show the potential of the proposed approach. The energy consumption prediction models achieved high prediction performance, and the thermal and visual comfort models were able to accurately represent the individual and group comfort levels. The optimization results showed potential behavioral energy savings in the range of 11% and 22%, with significant improvement in occupant comfort

Application de l'intelligence artificielle à la prédiction de la demande en eau chaude domestique et en électricité pour le contrôle par modèle prédictif dans les bâtiments résidentiels

Le secteur du bâtiment représente plus du tiers de la consommation énergétique et des émissions de gaz à effet de serre mondiales. Face à cet enjeu, des stratégies passives ont permis d'améliorer l'efficacité énergétique des bâtiments. À mesure que les technologies passives se rapprochent de leur limite physique d'efficacité, il devient nécessaire de s'intéresser à des technologies actives. Les stratégies de contrôle par modèle prédictif ont le potentiel de réduire la consommation énergétique des systèmes de chauffage, climatisation, ventilation, conditionnement de l'air et de production d'eau chaude domestique. Une difficulté limitant leur implantation dans les bâtiments provient du besoin de prédire des paramètres influencés par le comportement des occupantes et des occupants qui apparait stochastique, complexifiant le développement de modèles de prédiction. Dans ce contexte, cette thèse se concentre à évaluer des méthodes basées sur les données pour estimer la prédictibilité de la consommation d'eau chaude domestique et d'électricité dans un bâtiment résidentiel. L'impact d'une prédictibilité variable sur les performances est évalué lors de l'implémentation de ces modèles de prédiction dans des contrôleurs par modèle prédictif appliqués à des systèmes de production d'eau chaude domestique. Premièrement, la prédictibilité des profils de consommation d'eau chaude est évaluée à partir de profils mesurés dans un bâtiment résidentiel de 40 logements. Plus précisément, des réseaux de neurones sont entraînés à prédire cette consommation pour des systèmes de tailles variables allant d'un à 100 logements. Le niveau de prédictibilité est identifié comme étant proportionnel au nombre de logements et hautement variable pour des systèmes unifamiliaux, passant de très faible à élevé (c.-à-d., coefficient de détermination allant de 8 à 92% avec une moyenne de 58%). Les résultats montrent une difficulté à prédire précisément les pics de consommation, souvent sous-estimés lorsqu'une faible prédictibilité est observée. Puisqu'un contrôleur par modèle prédictif base ses décisions sur les prédictions, une faible prédictibilité pourrait impacter les performances en termes d'économie d'énergie et de respect des contraintes applicables à un système de production d'eau chaude. Deuxièmement, l'impact du niveau de prédictibilité des profils de consommation d'eau chaude sur les performances de contrôleurs par modèle prédictif est estimé. Les performances d'un contrôleur par modèle prédictif théorique employant des prédictions parfaitement précises sont comparées avec celles obtenues avec un contrôleur employant des prédictions imparfaites produites par les réseaux de neurones entraînés précédemment. Pour un système unifamilial, le principal effet des prédictions imparfaites sur les performances est le non-respect plus fréquent des contraintes de température dû à une incapacité à agir suffisamment en avance en préparation aux futurs pics de consommation d'eau chaude sous-estimés. Néanmoins, en comparaison avec une commande traditionnelle, des économies d'énergie allant de 4 à 8% ont été obtenues avec le contrôleur employant les prédictions imparfaites. En prédisant les périodes de pointe énergétique, les contrôleurs par modèle prédictif ont la capacité de réduire les pointes de consommation énergétique en déplaçant une partie de cette consommation vers les périodes hors-pointes. Dans cette optique, plusieurs modèles de prédiction basés sur les données sont entraînés afin de prédire la consommation d'électricité de logements unifamiliaux liée à l'éclairage et à l'utilisation des prises de courant sur plusieurs horizons allant de 10 minutes à 24 heures. Les arbres de décision renforcés (boosted) par le gradient sont identifiés comme étant la méthode produisant la meilleure qualité de prédiction. Une grande variabilité quant au niveau de prédictibilité est observée entre les logements, ce qui pourrait affecter la capacité des contrôleurs à réduire la consommation énergétique de pointe dans certains cas. Finalement, un dernier chapitre explore le potentiel d'un contrôleur par modèle prédictif employant les modèles de prédiction de la demande en eau chaude et de la consommation d'électricité pour prédire les périodes de pointe. Les résultats démontrent une plus grande différenciation entre les contrôleurs par modèle prédictif avec prédictions parfaites et imparfaites, le premier permettant de réduire d'avantage la consommation énergétique de pointe du chauffe-eau en prédisant plus précisément les périodes de pointe ainsi que la demande en eau chaude domestique correspondante. En comparaison avec la commande traditionnelle, des économies d'énergie pendant les périodes de pointe allant de 10 à 70% (moyenne de 26%) selon l'unité résidentielle étudiée ont été obtenues avec le contrôleur basé sur les prédictions imparfaites. Globalement, cette thèse représente un grand pas vers l'application future des contrôleurs par modèle prédictif basés sur l'apprentissage machine dans les bâtiments résidentiels, et les résultats obtenus démontrent le potentiel de cette stratégie de contrôle face à la réduction de la consommation d'énergie des systèmes de production d'eau chaude domestique unifamiliaux.The building sector accounts for more than a third of the worldwide energy consumption and greenhouse gas emissions. Facing these challenges, passive strategies have allowed to increase the energy efficiency of buildings. As these passive technologies are reaching their efficiency limits, it is necessary to turn our interest to active technologies. Model predictive control strategies have the potential to reduce the energy consumption of heating, cooling, ventilation and air conditioning as well as domestic hot water production systems. One of the challenges towards their application in buildings is the requirement to predict parameters that are influenced by occupants' behavior that appears to be stochastic. In this context, this thesis focuses on evaluating data-based methods to estimate the predictability of domestic hot water and electricity consumption profiles in a residential building. The impact of a varying predictability on the performance is evaluated by implementing these forecasting models in model predictive controllers applied to domestic hot water production systems. First, the predictability of domestic hot water consumption profiles is evaluated from profiles measured in a 40-unit case-study residential building. More specifically, neural networks are trained to predict this consumption for systems of varying size ranging between one and 100 units. The level of predictability is identified as proportional to the number of units and shows high variability for single-family systems, starting at very low and reaching high levels (i.e., coefficient of determination from 8 to 92% with a mean of 58%). Results show that accurately predicting consumption peaks is a challenge and often results in underestimating their amplitude when a low predictability is observed. As the decisions of model predictive controllers are based on predictions, a low predictability could impact their energy-saving performance and ability to respect the constraints of domestic hot water production systems. Thus, the impact of the level of predictability of hot water consumption profiles on the performance of model predictive controllers is estimated. The performance of a theoretical model predictive controller relying on perfectly accurate predictions are compared with that of a controller using imperfect predictions produced by the previously trained neural networks. In single-family systems, the main impact of imperfect predictions on the performance is more violations of the storage temperature constraint due to the inability to act sufficiently in advance in preparation of underestimated future hot water consumption peaks. Nonetheless, comparing with a traditional controller, energy savings from 4 to 8% were obtained with the predictive controller relying on imperfect forecasts. By predicting energy-peak periods, the predictive controllers have the ability to reduce peak energy consumption by moving parts of the energy consumption to off-peak periods. In this context, many data-based prediction models are trained to predict the plug load and lighting electricity consumption of single-family residential units over horizons of 10 minutes to 24 hours. Gradient-boosted regression trees are identified as the method providing the highest prediction quality. A high variability is observed for the level of predictability between residential units, which could affect the controllers' ability to reduce electricity consumption peaks in some cases. Finally, a last chapter explores the potential of a model predictive controller using the prediction models of the domestic hot water demand and of the electricity consumption to forecast electricity-peak periods. As the electricity consumption was demonstrated as challenging to predict in many contexts, the impact of forecasting inaccuracies on the performance of controllers is even more displayed here. The results show that the model predictive controllers with perfect or imperfect predictions are more differentiated, with the first managing to reduce more the electricity-consumption peaks of the water heater by accurately predicting peak periods along with the corresponding domestic hot water demand. Compared with a traditional controller, peak-period energy savings ranging from 10 to 70% (mean of 26%) were obtained with the controller relying on imperfect forecasts depending on the studied residential unit. Globally, this thesis is a major step towards future application of model predictive controllers based on machine learning in residential buildings. The results demonstrate the potential of this control strategy to reduce the energy consumption of single-family domestic hot water systems