Forecasting Binghamton University’s Future Electricity Consumption Using Building Classifications and Historical Weather Data as Inputs into a Machine Learning Model

With the rise of energy consumption intensifying the adverse impacts of carbon emissions, different alternatives to achieve sustainability and carbon neutrality are explored in academic conversations. Energy consumption forecasting, which refers to analyzing historical electricity usage to predict future trajectories, is a promising solution to achieve these visions of sustainability as this methodology enables organizations to identify their energy wastage to decrease their electricity usage, while also having the additional benefits of creating energy budgets and saving costs (Amber et al., 2017). My research specifically focuses on creating an energy consumption forecasting model for Binghamton University, using data from 2017-2020. To engineer this model, I will be classifying different university buildings and analyzing the correlation of these building’s electricity usage patterns with historical weather data. This information will then be processed through a machine learning model using AzureML Studio to forecast future electricity usage patterns. By presenting a model for forecasting future energy consumption utilizing building classifications and historical weather data as inputs, my research will illustrate the accuracy of energy consumption forecasting. I specifically argue that Binghamton University should implement this energy usage forecasting model as they have the potential to reduce carbon emissions, help financial budgeting, and minimize energy expenses.https://orb.binghamton.edu/research_days_posters_2022/1000/thumbnail.jp

Forecasting Solar Home System Customers’ Electricity Usage with a 3D Convolutional Neural Network to Improve Energy Access

Off-grid technologies, such as solar home systems (SHS), offer the opportunity to alleviate global energy poverty, providing a cost-effective alternative to an electricity grid connection. However, there is a paucity of high-quality SHS electricity usage data and thus a limited understanding of consumers’ past and future usage patterns. This study addresses this gap by providing a rare large-scale analysis of real-time energy consumption data for SHS customers (n = 63,299) in Rwanda. Our results show that 70% of SHS users’ electricity usage decreased a year after their SHS was installed. This paper is novel in its application of a three-dimensional convolutional neural network (CNN) architecture for electricity load forecasting using time series data. It also marks the first time a CNN was used to predict SHS customers’ electricity consumption. The model forecasts individual households’ usage 24 h and seven days ahead, as well as an average week across the next three months. The last scenario derived the best performance with a mean squared error of 0.369. SHS companies could use these predictions to offer a tailored service to customers, including providing feedback information on their likely future usage and expenditure. The CNN could also aid load balancing for SHS based microgrids

Application of multiple regression analysis to forecasting South Africa’s electricity demand

In a developing country such as South Africa, understanding the expected future demand for electricity is very important in various planning contexts. It is specifically important to understand how expected scenarios regarding population or economic growth can be translated into corresponding future electricity usage patterns. This paper discusses a methodology for forecasting long-term electricity demand that was specifically developed for applying to such scenarios. The methodology uses a series of multiple regression models to quantify historical patterns of electricity usage per sector in relation to patterns observed in certain economic and demographic variables, and uses these relationships to derive expected future electricity usage patterns. The methodology has been used successfully to derive forecasts used for strategic planning within a private company as well as to provide forecasts to aid planning in the public sector. This paper discusses the development of the modelling methodology, provides details regarding the extensive data collection and validation processes followed during the model development, and reports on the relevant model fit statistics. The paper also shows that the forecasting methodology has to some extent been able to match the actual patterns, and therefore concludes that the methodology can be used to support planning by translating changes relating to economic and demographic growth, for a range of scenarios, into a corresponding electricity demand. The methodology therefore fills a particular gap within the South African long-term electricity forecasting domain

Application of multiple regression analysis to forecasting South Africa’s electricity demand

In a developing country such as South Africa, understanding the expected future demand for electricity is very important in various planning contexts. It is specifically important to understand how expected scenarios regarding population or economic growth can be translated into corresponding future electricity usage patterns. This paper discusses a methodology for forecasting long-term electricity demand that was specifically developed for applying to such scenarios. The methodology uses a series of multiple regression models to quantify historical patterns of electricity usage per sector in relation to patterns observed in certain economic and demographic variables, and uses these relationships to derive expected future electricity usage patterns. The methodology has been used successfully to derive forecasts used for strategic planning within a private company as well as to provide forecasts to aid planning in the public sector. This paper discusses the development of the modelling methodology, provides details regarding the extensive data collection and validation processes followed during the model development, and reports on the relevant model fit statistics. The paper also shows that the forecasting methodology has to some extent been able to match the actual patterns, and therefore concludes that the methodology can be used to support planning by translating changes relating to economic and demographic growth, for a range of scenarios, into a corresponding electricity demand. The methodology therefore fills a particular gap within the South African long-term electricity forecasting domain

Understanding Electricity-Theft Behavior via Multi-Source Data

Electricity theft, the behavior that involves users conducting illegal operations on electrical meters to avoid individual electricity bills, is a common phenomenon in the developing countries. Considering its harmfulness to both power grids and the public, several mechanized methods have been developed to automatically recognize electricity-theft behaviors. However, these methods, which mainly assess users' electricity usage records, can be insufficient due to the diversity of theft tactics and the irregularity of user behaviors. In this paper, we propose to recognize electricity-theft behavior via multi-source data. In addition to users' electricity usage records, we analyze user behaviors by means of regional factors (non-technical loss) and climatic factors (temperature) in the corresponding transformer area. By conducting analytical experiments, we unearth several interesting patterns: for instance, electricity thieves are likely to consume much more electrical power than normal users, especially under extremely high or low temperatures. Motivated by these empirical observations, we further design a novel hierarchical framework for identifying electricity thieves. Experimental results based on a real-world dataset demonstrate that our proposed model can achieve the best performance in electricity-theft detection (e.g., at least +3.0% in terms of F0.5) compared with several baselines. Last but not least, our work has been applied by the State Grid of China and used to successfully catch electricity thieves in Hangzhou with a precision of 15% (an improvement form 0% attained by several other models the company employed) during monthly on-site investigation.Comment: 11 pages, 8 figures, WWW'20 full pape

Predicting Electrical Power Consumption on Yearly Events for Substations: Algorithm Design and Performance Evaluations

Master's thesis in Information- and communication technology (IKT590)Accurate prediction of electricity usage is critical for grid companies in or-der to ensure reliable power supply for their customers. Many factors in-fluence usage patterns, but generally they consist of yearly-, weekly- and daily trends in addition to stochastic noise due to random user behaviour. Besides the above-mentioned cyclic trends, certain yearly events, i.e. events that take place once per year, can affect usage patterns significantly and thus may cause abnormally high or -low power consumption. Therefore, it is in the interest of grid companies to predict the consumption on such events so they can take measures in advance, if necessary. Much effort has been put into developing methods of improving forecasting accuracy through the use of time series clustering in conjunction with the actual prediction algorithm, but the methods’ ability to specifically improve the prediction of power consumption on yearly events has not yet been evaluated. In this the-sis, we are going to utilize machine learning algorithms to cluster electricity usage patterns and predict power consumption for yearly events based on real operational data at the substation level. More specifically, groups of similar usage profiles are formed by a clustering algorithm, and rather than training a prediction model on a single time series, a similar series from the same cluster is appended. In order to extend the prediction model’s training set in this manner, the appended time series is transformed to fit the scale of the initial time series. Our experiments reveal that combining similar time series, thereby introducing additional yearly events to the prediction model’s training set, can improve the accuracy of the load forecast on the event. This approach is also capable of compensating for missing events in the initial time series, when present in the appended-, similarly behaving time series

Small Firm Electricity Demand in Las Cruces, New Mexico, USA

Research examining small commercial and industrial electricity usage patterns have historically received less attention than residential electricity consumption patterns. This study examines electricity as an input to small firm commercial and industrial (CIS) production in Las Cruces, the second largest metropolitan economy in the state of New Mexico, using annual frequency data from 1978 to 2018. Those data include labor, per capita personal income, price measures for electricity and natural gas, and weather variables. The long-run and short-run elasticities of the data are then estimated using an autoregressive distributed lag model (ARDL). In the long-run, the CIS derived-demand curve is found to be upward sloping, and Las Cruces CIS customers use natural gas as a complementary input. Real per capita income is also found to have a positive impact in the long-run, while weather impacts are found to be ambiguous. In the short-run, the Las Cruces CIS derived-demand curve is downward sloping, CIS customers use natural gas as a substitute factor, and weather extremes are found to be positively correlated with small firm electricity usage

A cluster-based baseline load calculation approach for individual industrial and commercial customer

Demand response (DR) in the wholesale electricity market provides an economical and efficient way for customers to participate in the trade during the DR event period. There are various methods to measure the performance of a DR program, among which customer baseline load (CBL) is the most important method in this regard. It provides a prediction of counterfactual consumption levels that customer load would have been without a DR program. Actually, it is an expected load profile. Since the calculation of CBL should be fair and simple, the typical methods that are based on the average model and regression model are the two widely used methods. In this paper, a cluster-based approach is proposed considering the multiple power usage patterns of an individual customer throughout the year. It divides loads of a customer into different types of power usage patterns and it implicitly incorporates the impact of weather and holiday into the CBL calculation. As a result, different baseline calculation approaches could be applied to each customer according to the type of his power usage patterns. Finally, several case studies are conducted on the actual utility meter data, through which the effectiveness of the proposed CBL calculation approach is verified

Electricity Consumption Prediction in Oil and Gas Equipment Service and Maintenance Workshops Using RNN LSTM

This research offers a Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) model for forecasting power usage in a facility that provides oil and gas equipment service and maintenance. The model was used using hourly electricity consumption data. The LSTM model was chosen because of its compatibility with time-series data and its capacity to capture temporal dependencies and patterns in sequential data, which may be utilized to predict future consumption. Experiments were undertaken in this study to determine the ideal model parameters and evaluate the model’s accuracy using the root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) metrics. The findings demonstrated that the suggested model accurately predicted electricity usage with a MAPE of 3%. The quality and quantity of available data for the training dataset may, however, affect the accuracy of the model. Overall, our results indicate that the suggested RNN LSTM model can properly estimate factory power use

Implementation of Artificial Neural Network Method for Estimating Connected Power and Electric Energy Consumption

Abstract—Electricity is vital for modern society’s welfare. Daily electricity usage depended on the customers’ type. Hence, there was a difference between the connected power with consumption. Therefore, there needed an estimation method for long-term connected power and energy consumption to improve the safety of energy management and operation plan for the generator. This research used the Artificial Neural Network method with a backpropagation algorithm model to estimate the connected power and electricity consumption. This method has the advantage of following past patterns after the training process. This research used data such as total population, Gross Regional Domestic Product, total customers, produced energy, remaining energy, distribution loss, total transformer, peak load, and load factor as the independent data. The energy consumption and connected power served as the dependent data. The data was taken from Srengat Network Service Unit, East Java, for ten years, which started in 2008. This research used literature study, information and data collection, information and data process, data estimation and analysis, and conclusion as the procedures. Based on the results, the best network structure was 9-9-2 with the 10-6 goal, 0.9 momentum value, and 0.15 learning rate to produce the smallest Mean Squared Error of 0.00442 in 2015, Mean Absolute Percentage Error of 7.88% for the connected power, and 11.27% on electricity consumption target