Rule-based Autoregressive Moving Average Models for Forecasting Load on Special Days: A Case Study for France

This paper presents a case study on short-term load forecasting for France, with emphasis on special days, such as public holidays. We investigate the generalisability to French data of a recently proposed approach, which generates forecasts for normal and special days in a coherent and unified framework, by incorporating subjective judgment in univariate statistical models using a rule-based methodology. The intraday, intraweek, and intrayear seasonality in load are accommodated using a rule-based triple seasonal adaptation of a seasonal autoregressive moving average (SARMA) model. We find that, for application to French load, the method requires an important adaption. We also adapt a recently proposed SARMA model that accommodates special day effects on an hourly basis using indicator variables. Using a rule formulated specifically for the French load, we compare the SARMA models with a range of different benchmark methods based on an evaluation of their point and density forecast accuracy. As sophisticated benchmarks, we employ the rule-based triple seasonal adaptations of Holt-Winters-Taylor (HWT) exponential smoothing and artificial neural networks (ANNs). We use nine years of half-hourly French load data, and consider lead times ranging from one half-hour up to a day ahead. The rule-based SARMA approach generated the most accurate forecasts.Comment: 11 figures, 3 table

Forecasting Intraday Time Series with Multiple Seasonal Cycles Using Parsimonious Seasonal Exponential Smoothing

This paper concerns the forecasting of seasonal intraday time series. An extension of Holt-Winters exponential smoothing has been proposed that smoothes an intraday cycle and an intraweek cycle. A recently proposed exponential smoothing method involves smoothing a different intraday cycle for each distinct type of day of the week. Similar days are allocated identical intraday cycles. A limitation is that the method allows only whole days to be treated as identical. We introduce an exponential smoothing formulation that allows parts of different days of the week to be treated as identical. The result is a method that involves the smoothing and initialisation of fewer terms than the other two exponential smoothing methods. We evaluate forecasting up to a day ahead using two empirical studies. For electricity load data, the new method compares well with a range of alternatives. The second study involves a series of arrivals at a call centre that is open for a shorter duration at the weekends than on weekdays. By contrast with the previously proposed exponential smoothing methods, our new method can model in a straightforward way this situation, where the number of periods on each day of the week is not the same.Exponential smoothing; Intraday data; Electricity load; Call centre arrivals.

Proportional-Integral-Plus Control Strategy of an Intelligent Excavator

This article considers the application of Proportional-Integral-Plus (PIP) control to the Lancaster University Computerised Intelligent Excavator (LUCIE), which is being developed to dig foundation trenches on a building site. Previous work using LUCIE was based on the ubiquitous PI/PID control algorithm, tuned on-line, and implemented in a rather ad hoc manner. By contrast, the present research utilizes new hardware and advanced model-based control system design methods to improve the joint control and so provide smoother, more accurate movement of the excavator arm. In this article, a novel nonlinear simulation model of the system is developed for MATLAB/SIMULINK, allowing for straightforward refinement of the control algorithm and initial evaluation. The PIP controller is compared with a conventionally tuned PID algorithm, with the final designs implemented on-line for the control of dipper angle. The simulated responses and preliminary implementation results demonstrate the feasibility of the approach

Lusitania Episode

Department of Histor

Organizational change and inclusive practices: Promoting access for diverse populations in the Canadian Mental Health Association (Waterloo Region Branch) (Ontario)

This research began a process and generated information that would help guide the Canadian Mental Health Association/Waterloo Region Branch (CMHA/WRB) in developing services that meet the needs of all residents in the area it serves. This project was comprised of two phases. The phases were conceptualized as being intervention cycles consisting of information, awareness and action-building components. The first phase consisted of work done within the agency itself, to help articulate the goals, attitudes, and possible barriers seen by the paid/non-paid staff towards the new multicultural emphasis. This work involved three focus groups with paid and non-paid staff. As well, a demographic profile was created to examine the demographic trends and composition of the Region. The second phase of the research involved consultation with the specific ethnic communities and other service providers in the community to help understand help-seeking patterns, barriers to service and mental health issues of the multicultural community. This phase consisted of a focus group and a community forum. Overall, past research in the community (Alcalde, 1992; Kramer, 1991) and the people consulted in this project have identified a variety of issues and barriers facing the multicultural community. These include: - Outreach. Segments of the community are not aware of CMHA services. A need has been expressed for CMHA services and resources both within this project and others (Alcalde, 1992). - Language and communication barriers. Language and communication barriers are a primary issue for the multicultural community and need to be addressed if people are to have fair and equitable access to services. - Need for information. Lack of information on services and mental health education is a significant barrier and mental health issue for the multicultural community. - Staff training. Cultural sensitivity training can be key in helping make services more accessible. Such training will help staff and volunteers work more effectively with multicultural clients by providing new tools, resources and understanding. - Networking and partnerships. Networking can help identify community needs and the supports and resources necessary for the agency to address those needs. Building partnerships is one significant response that can be taken to address the above issues. Connecting with other groups and key people from the multicultural community can provide the guidance and knowledge that the agency needs to address multicultural issues. Although the information generated in this project is not new, the process of generating this information has lead to increased awareness and action at the community and agency level. This project has acted as an initial bridge between the multicultural community and the agency. Future actions need to build on the bridging concept as the agency continues its efforts to become more inclusive

A Comparison of Aggregation Methods for Probabilistic Forecasts of COVID-19 Mortality in the United States

The COVID-19 pandemic has placed forecasting models at the forefront of health policy making. Predictions of mortality and hospitalization help governments meet planning and resource allocation challenges. In this paper, we consider the weekly forecasting of the cumulative mortality due to COVID-19 at the national and state level in the U.S. Optimal decision-making requires a forecast of a probability distribution, rather than just a single point forecast. Interval forecasts are also important, as they can support decision making and provide situational awareness. We consider the case where probabilistic forecasts have been provided by multiple forecasting teams, and we aggregate the forecasts to extract the wisdom of the crowd. With only limited information available regarding the historical accuracy of the forecasting teams, we consider aggregation (i.e. combining) methods that do not rely on a record of past accuracy. In this empirical paper, we evaluate the accuracy of aggregation methods that have been previously proposed for interval forecasts and predictions of probability distributions. These include the use of the simple average, the median, and trimming methods, which enable robust estimation and allow the aggregate forecast to reduce the impact of a tendency for the forecasting teams to be under- or overconfident. We use data that has been made publicly available from the COVID-19 Forecast Hub. While the simple average performed well for the high mortality series, we obtained greater accuracy using the median and certain trimming methods for the low and medium mortality series. It will be interesting to see if this remains the case as the pandemic evolves.Comment: 32 pages, 11 figures, 5 table

Tidal Destruction of The First Dark Microhalos

We point out that the usual self-similarity in cold dark matter models is broken by encounters with individual normal galactic stars on sub-pc scale. Tidal heating and stripping must have redefined the density and velocity structures of the population of the Earth-mass dark matter halos, which are likely to have been the first bound structures to form in the Universe. The disruption rate depends strongly on {\it galaxy types} and the orbital distribution of the microhalos; in the Milky Way, stochastic radial orbits are destroyed first by stars in the triaxial bulge, microhalos on non-planar retrograde orbits with large pericenters and/or apocenters survive the longest. The final microhalo distribution in the {\it solar neighborhood} is better described as a superposition of filamentry microstreams rather than as a set of discrete spherical clumps in an otherwise homogeneous medium. We discuss its important consequences to our detections of microhalos by direct recoil signal and indirect annihilation signal.Comment: 13 pages, 3 figures, Astrophysical Journal, accepte

Short-term Forecasting of Anomalous Load Using Rule-based Triple Seasonal Methods

Numerous methods have been proposed for forecasting load for normal days. Modeling of anomalous load, however, has often been ignored in the research literature. Occurring on special days, such as public holidays, anomalous load conditions pose considerable modeling challenges due to their infrequent occurrence and significant deviation from normal load. To overcome these limitations, we adopt a rule-based approach, which allows incorporation of prior expert knowledge of load profiles into the statistical model. We use triple seasonal Holt-Winters-Taylor (HWT) exponential smoothing, triple seasonal autoregressive moving average (ARMA), artificial neural networks (ANNs), and triple seasonal intraweek singular value decomposition (SVD) based exponential smoothing. These methods have been shown to be competitive for modeling load for normal days. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model load for special days, when used in conjunction with a rule-based approach. The proposed rule-based method is able to model normal and anomalous load in a unified framework. Using nine years of half-hourly load for Great Britain, we evaluate point forecasts, for lead times from one half-hour up to a day ahead. A combination of two rule-based methods generated the most accurate forecasts.Comment: 8 Pages, 11 Figure