Download or read book Forecasting Energy Demand Peak Load Days with the Inclusion of Solar Energy Production written by Connor Rollins and published by . This book was released on 2020 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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."--Abstract.
Download or read book A Customer Agnostic Machine Learning Based Peak Electric Load Days Forecasting Methodology for Consumers with and Without Renewable Electricity Generation written by Omar Aponte and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The adoption of electricity generation from renewable sources, as well as the push for a speedy electrification of sectors such as transportation and buildings, makes peak electric load management an essential aspect to ensure the electric grid’s reliability and safety. Utilities have established peak load charges that can amount to up to 70% of electricity costs to transfer the financial burden of managing these loads to the consumers. These pricing schemes have created a need for efficient peak electric load management strategies that consumers can implement in order to reduce the financial impact of this type of load. Research has shown that the impact of peak load charges can be reduced by acting on the intelligence provided by peak electric load days (PELDs) forecasts. Unfortunately, published PELDs forecasting methodologies have not addressed the increasing number of facilities adopting behind the meter renewable electricity generation. The presence of this type of intermittent generation adds substantial complexity and other challenges to the PELDs forecasting process. The work reported in this dissertation is organized in terms of its three main contributions to the body of knowledge and to society. First, the development and testing of a first of its kind PELDs forecasting methodology able to accurately predict upcoming PELDs for a consumer regardless of the presence or absence of renewable electricity generation. Experimental results showed that 93% and 90% of potential savings (approximately US$ 142,129.01 and US$ 123,100.74) could be achieved by a consumer with and a consumer without behind the meter solar generation respectively. The second contribution is the development and testing of a novel methodology that allows virtually any type of consumer to determine an efficient electricity demand threshold value before the start of a billing period. This threshold value allows consumers to proactively trigger demand response actions and reduce peak demand charges without receiving any type of signal or information from the utility. Experimental results showed 65% to 82% of total potential demand charge reductions achieved during a year for three different consumers: residential, industrial, and educational with solar generation. These results translate to US$ 149.09, US$ 23,290.00, and US$ 107,610.00 in demand charges savings a year respectively. As a third contribution, we present experimental results that show how the implementation of machine learning based ensemble classification techniques improves the PELDs forecasting methodology’s performance beyond previously published ensemble techniques for three different consumers."--Abstract.
Download or read book Solar Irradiance Forecasting Using Hybrid Ensemble Machine Learning Technique written by Josalin Jemima J and published by Mohammed Abdul Sattar. This book was released on 2024-01-02 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Economic development is impacted significantly by conventional energy sources, which are hazardous to humans and the environment. To meet the energy demand and reduce greenhouse gas emissions, the world is shifting towards alternate renewable energy sources. Photovoltaics (PV) is the most common distributed energy source for microgrid formation and one of the world's top renewable energy sources because of their modular design, minimal operational noise, and ease of maintenance. Solar photovoltaic systems, which are photovoltaic panels that turn sunlight into electricity, are one of the most common renewable energy sources. PV production is strongly dependent on solar irradiation, temperature, and other weather conditions. Predicting solar irradiance implies predicting solar power generation one or more steps ahead of time. Prediction increases photovoltaic system development and operation while providing numerous economic benefits to energy suppliers. There are numerous applications that employ prediction to improve power grid operation and planning, with the appropriate time-resolution of the forecast. Stability and regulation necessitate knowledge of solar irradiation over the following few seconds. Reserve management and load following require knowledge of solar irradiation for the next several minutes or hours. To function properly, scheduling and unit commitment requires knowledge about the next few days of solar irradiation. It is crucial to precisely measure solar irradiation since the major issue with solar energy is that it fluctuates because of its variability. Grid operators can control the demand and supply of power and construct the best solar PV plant with the help of accurate and reliable solar irradiance predictions. Electric utilities must generate enough energy to balance supply and demand. The electric sector has consequently focused on Solar PV forecasting to assist its management system, which is crucial for the growth of additional power generation, such as microgrids. Forecasting solar irradiance has always been important to renewable energy generation since solar energy generation is location and time-specific. When the estimated solar generation is available, the grid will function more consistently in unpredictable situations since solar energy generates some quantity of power every day of the year, even on cloudy days.
Download or read book Forms and Instructions for Electricity Demand Forecasts written by Nick Fugate and published by . This book was released on 2012 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Energy Forecasting written by Terry H. Morlan and published by . This book was released on 1985 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Regional Medium Term Hourly Electricity Demand Forecasting Based on LSTM Preprint written by and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decompose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep-learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high-level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for a one-year lead hourly load below 5% MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.
Download or read book Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications written by M. Sengupta and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Future of solar photovoltaic written by International Renewable Energy Agency IRENA and published by International Renewable Energy Agency (IRENA). This book was released on 2019-11-01 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study presents options to fully unlock the world’s vast solar PV potential over the period until 2050. It builds on IRENA’s global roadmap to scale up renewables and meet climate goals.
Download or read book Forecasting Strategies for Predicting Peak Electric Load Days written by Harshit Saxena and published by . This book was released on 2017 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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."--Abstract.
Download or read book Energy Abstracts for Policy Analysis written by and published by . This book was released on 1982 with total page 848 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Characterizing and Mitigating the Impact of Solar Forecast Errors on Grid Planning and Operations written by Guang Chao Wang and published by . This book was released on 2019 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, the contribution of photovoltaic (PV) power production to the electric grid has been increasing. Still, a number of challenges remain for a reliable and efficient integration of solar energy. While conventional electric power generated by gas turbines can be adjusted to follow the grid load, the stochastic nature of solar radiation makes it difficult to control the PV output, which hinders its integration in the grid. Accurate solar forecasts help grid operators integrate solar energy by enhancing power quality and reducing grid operation costs. Following the development of sky imager hardware and algorithms at UC San Diego, we present a variety of models and methodologies to reduce sky imager forecasts errors by improving the accuracy of meteorological parameters, compensating the power mismatch caused by solar forecasts errors, and mitigating the impact of solar forecast errors on real world grid planning and operations. First, a low-cost instrument for measuring local cloud motion vectors (CMVs) was developed. Three algorithms for estimating local cloud base height (CBH) using a single sky imager paired with either distributed ground irradiance sensors or measured CMVs were then designed and tested. Since sky imager forecasts are often used in conjunction with other instruments for measuring CBH, cloud velocity, and/or solar irradiance measurements, our approaches decrease instrumentation costs and logistical complexity. More importantly, through these algorithms, local measurements improve sky imager forecasts by adding information that is unobservable from a single sky imager. Second, integrating battery systems into a PV plant can compensate the power imbalance caused by solar forecast errors. Battery system size can be optimized by determining the energy reserve required to offset the possible maximum power ramp. Because passing cloud shadows are the main cause of the power ramps, a simple model based on physics variables that are available globally can determine the worst power ramp rates. Local CMV measurements enable even more accurate maximum ramp rate estimates. The key merit of the method is that it is universally applicable in the absence of high frequency measurements. Finally, issues when integrating imperfect solar forecasts in grid operations are evaluated. Both physics-based forecasts and Numerical Weather Prediction (NWP) based machine learning forecasts that are commonly utilized in the grid operations exhibit autocorrelated forecast errors. First, a deterministic valley-filling problem through EV charging is formulated to investigate how the autocorrelated forecast errors increase peak demand and cause grid net load variability. Then a corrective optimization framework is proposed to minimize the deviation of the realistic valley filling solutions from the ideal solutions. In addition, with the goal of operational deployment, stochastic programming incorporating real time updates of solar forecast and EV charge events to address real-world uncertainty is employed. The optimal valley filling problem is solved in an innovative way and executed under a predictive control scheme in the presence of autocorrelated forecast errors. The proposed corrective stochastic optimization framework successfully mitigates the impact of autocorrelated forecasts errors on grid operations.
Download or read book Global Challenges for the Environment and Climate Change written by Idris, Sofia and published by IGI Global. This book was released on 2024-07-22 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rampant deforestation, rising carbon emissions, and more unprecedented threats are creating chaos and turmoil for the environment. The delicate balance between nature and humanity seems to waver on the brink of collapse. Climate change exacerbates standard occurrences of natural disasters, and endangers countless species. Amid these daunting challenges, the need for comprehensive research and actionable solutions has never been greater. Global Challenges for the Environment and Climate Change draws upon the latest research and empirical findings, and offers a roadmap for navigating the complexities of our interconnected world. Exploring topics such as climate change, sustainable consumption, and global governance equips readers with the knowledge and insights needed to effect meaningful change.
Download or read book Draft Demand Forecast Forms and Instructions for the 2007 Integrated Energy Policy Report written by and published by . This book was released on 2006 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Global Energy Assessment written by Thomas B. Johansson and published by Cambridge University Press. This book was released on 2012-08-27 with total page 1885 pages. Available in PDF, EPUB and Kindle. Book excerpt: Independent, scientifically based, integrated, policy-relevant analysis of current and emerging energy issues for specialists and policymakers in academia, industry, government.
Download or read book Operating and Planning Electricity Grids with Variable Renewable Generation written by Marcelino Madrigal and published by World Bank Publications. This book was released on 2013-03-01 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: The paper Challenges and Approaches to Electricity Grids Operations and Planning with Increased Amounts of Variable Renewable Generation: Emerging Lessons from Selected Operational Experiences and Desktop Studies focuses on analyzing the impacts of variable renewable energy on the operation and planning
Download or read book The costs and impacts of intermittency written by and published by . This book was released on 2006 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Comprehensive Energy Systems written by Ibrahim Dincer and published by Elsevier. This book was released on 2018-02-07 with total page 5543 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive Energy Systems, Seven Volume Set provides a unified source of information covering the entire spectrum of energy, one of the most significant issues humanity has to face. This comprehensive book describes traditional and novel energy systems, from single generation to multi-generation, also covering theory and applications. In addition, it also presents high-level coverage on energy policies, strategies, environmental impacts and sustainable development. No other published work covers such breadth of topics in similar depth. High-level sections include Energy Fundamentals, Energy Materials, Energy Production, Energy Conversion, and Energy Management. Offers the most comprehensive resource available on the topic of energy systems Presents an authoritative resource authored and edited by leading experts in the field Consolidates information currently scattered in publications from different research fields (engineering as well as physics, chemistry, environmental sciences and economics), thus ensuring a common standard and language
