While microgrids can be seen exclusively as generators, the penetration of renewable energies has brought electricity generation capabilities to individual users. The most popular situation is the one of solar panels, which can easily be installed on rooftops, but there are also examples of small wind turbines and CHP systems employed by more remote households/farms. Additionally, electric vehicles can behave both as consumers and storage facilities in case of emergency, being able to provide energy back to the household when necessary. The collaboration of households having the purpose of efficiently using the energy produced develops further towards the concept of a VPP. If we consider exclusively the generation facilities of a community of such users, the resulted VPP can be seen as a microgrid as well. We envisage such a system in a cooperative environment, where the subnetwork belonging to a transformer allows users to exchange electricity therefore minimizing power flow from the main grid. More- over, electricity can be sold back to the grid for the sake of maximizing the use of renewables. These decisions heavily rely on the future electricity usage of the community, which cannot be predefined due to the nature of human behaviour. Day-ahead demandforecasting is therefore employed in such cases.
The socioeconomic and cultural behavior of a population may be reflected in the consumption of electrical energy. Due to the foregoing, researchers and academics have developed models to predict electricity demand in the short, medium and long term. This paper presents an Artificial Neural Network (ANN) for the prediction of daily electricity demand (GWh) in small Colombian populations. The methodology proposed by Kaastra and Boyd is used for the construction, training and validation of the network and the development of the model in the statistical software SPSS. This paper conclude that the predicted values with models constructed with Artificial Neural Networks (ANN) present a greater degree of approach with the real values of electricity demand (GWh). Also it indicates that the values obtained using models developed with other forecasting techniques (game theory, time series, simulation models, and others) allow to include variables and external factors that are difficult to quantify with simple equations.
Abstract: The forecasting of electricity demand has become one of the major research fields in Electrical Engineering. In recent years, much research has been carried out on the application of artificial intelligence techniques to the Load-Forecasting problem. Various Artificial Intelligence (AI) techniques used for load forecasting are Expert systems, Fuzzy, Genetic Algorithm, Artificial Neural Network (ANN). This research work is an attempt to apply hybrid and integrated effort to forecast load. Regression, Fuzzy and Neural along with Genetic Algorithm will empower the analysts to strongly forecast fairly accurate load demand on hourly base.
Nowadays, with the advance of renewable energy, electrical energy supplied to the road can be saved. Increasing usage of electrical energy that uses non-renewable energy will lead to pollution. Thus, to replace the conventional non-renewable sources is by develop a clean and renewable energy such as solar and wind energy. One of the most promising applications related to renewable energy is the hybrid energy technology stated by Daniele et al, 2013. Hybrid systems are the ones that use more than one energy resources. According to Fesli et al, 2009 it is possible to have any combination of energy resources to supply the energy demand in the hybrid systems, such as solar and wind. Integration of systems (wind and solar) has more influence in terms of electric power production.
From a technological point of view, ICT based solutions, such as Supervisory Control And Data Acquisition (SCADA) systems are already widely adopted to monitor and control WDNs, able to report warnings and alarms triggered on specific rules, as well to store data for further analytical approaches, such as advanced functionalities for leakage management . With respect to water usage and consumption behavior of individual customers, Automatic Metering Readers (AMR) are devices which are gaining new interest in the field of “smart water”. Since their deployment involves all the customers of the water utility, AMRs are more expensive than SCADA. However, the availability of huge amount of high-rate consumption data permits to achieve a more accurate customer-segmentation, to define specific demand management strategies and to perform individual demandforecasting.
The next course of action involves the actual generation of the forecasted values within the UFM Update process. This process is designed to run continuously, and the UFM engineers the methodology to self adjust parameters in order to maintain their optimality and update metadata associated with demand entities. Advanced demand cleansing and seasonal profiling logic is applied here as well. Additional logic specific to the UFM’s management of intermittent signals is also conducted here. The process proactively handles periods of zero demand in a manner optimal for inventory control optimization of both safety and cycle stocks.
Leading indicators The demand for many products is derived from some other activity, the statistics for which can provide the basis of a forecast for the product in question. For example, CSR Building Materials has much of its demand derived from housing starts and building approvals, which in turn are dependent to a large extent on the health of the economy. Many leading indicators are supplied to the building industry through the Housing Industry Association, the Indicative Planning Association and BIS Shrapnel. Economic indicators such as the Consumer Price Index (CPI) and the All-ordinaries Share Index are also available for use in forecasting.
ABSTRACT: To meet the fast growing demand of energy, smart techniques need to be adopted that are in compliance with the environment and energy conservation. In this paper, an autonomous demand-side energy management to encourage users to willingly modify their electricity consumption without compromising with service quality and customer satisfaction using load forecasting. The projected distributed demand side energy management (DSM) strategy gives each consumer an option to simply apply its best response strategy to current electric Load and tariff in the power distribution system. Using NN and ACO technique on load prediction, it is obtained that an area-load based pricing method is beneficial for both electric utility and consumer. Simulation results shows that the proposed approach can maximize load factor and reduce total energy cost as well as user’s daily electricity charges.
Shvartser et al. ( 1993 ) combined the pattern recognition tech- nique with the time-series analysis to forecast the water demand. The model performed the pattern recognition for three segments of the daily cycle, ascending, oscillating, and descending phase, as “states” of the water demand curve, which can be described as successive states of a Markov process. Then the transition probabil- ities between the states were calculated and each segment fitted by an ARIMA model. The model obtained was applied to Israel ’s water supply, which, according to the author, produced satisfactory results. Other examples of short-term water-demandforecasting models on the basis of time-series analysis include Valdes and Sastri ( 1989 ); Miaou ( 1990 ); and Jowitt and Xu ( 1992 ). However, the online forecast research which considered the consumption data as a dynamic sequence of values with parameters constantly up- dated in time represents a real breakthrough. In this regard, the models developed by Perry ( 1981 ) and Zahed ( 1990 ) stand out among those presented in the literature. Perry ( 1981 ) decomposed the consumption sequence in a series of harmonics and residual flows, which were fitted by an ARMA model. Both the harmonic coefficients and the ARMA coefficients, which were dynamically updated, were obtained from a time series and used for a 24-h fore- cast. Zahed ( 1990 ), on the basis of Perry ’s model ( Perry 1981 ), proposed and applied two-hour water demandforecasting in the operation of part of the metropolitan pipeline system of the city of São Paulo, Brazil. One was on the basis of the Taylor series and the other on the Fourier series. As important premises of the former model, he stated the elimination of the weekday effect by adjusting the harmonic coefficients to the consumption of a week and by an hourly update of them. Additionally, recent data were used to refine the forecast instead of auxiliary models.
A. Overall Performance of Selected Forecasting Models This thesis implemented different methods and models for forecasting aircraft orders and deliveries. Based on the results presented in the previous section, it is first important to note that all forecasting techniques were deemed more accurate than the Naïve No-Change forecast, according to Theil’s U. This indicates that each forecast is more sophisticated than the most rudimentary method and was sufficient for further analysis.
The limitation of a forecast with a single expected outcome is clear: while it may provide the single best guess, it offers no information about the range of probable outcomes. The problem becomes acute when uncertainty surrounding the underlying assumptions of the forecast is especially high. The high case-low case approach can actually exacerbate this problem because it gives no indication of how likely it is that the high and low cases will actually materialize. Indeed, the high case usually assumes that most underlying assumptions deviate in the same direction from their expected value; and likewise for the low case. In reality, the likelihood that all underlying factors shift in the same direction simultaneously is just as remote as everything turning out as expected.
Methods for forecasting intermittent demand are compared using a large data-set from the UK Royal Air Force (RAF). Several important results are found. First, we show that the traditional per period forecast error measures are not appropriate for intermittent demand, even though they are consistently used in the literature. Second, by comparing target service levels to achieved service levels when inventory decisions are based on demand forecasts, we show that Croston’s method (and a variant) and Bootstrapping clearly outperform Moving Average and Single Exponential Smoothing. Third, we show that the performance of Croston and Bootstrapping can be significantly improved by taking into account that each lead time starts with a demand.
34. The other aspect that troubles us is that considerable activity is unfolding in the energy-regulation space that will impact the shape of demand and generation in the near future. Transmission and distribution pricing reviews are likely to result in a reallocation of costs across consumers which in turn would likely see a demand response. This activity needs to be undertaken with a well-founded view of the possible ‘demand/generation’ futures and uncertainties agreed across system participants. While system participants will have differing views on aspects of the system that are driven by their objectives and incentives, it makes little sense to us to have views of the future that cannot be reconciled. 35. Our views on the reality of the changes that are underway are consistent with
Forecasting management is a complicated issue and companies can determine to control different aspects to improve their forecasting process (Mentzer and Bienstock, 1998; Moon, Mentzer and Smith, 2003). Forecasting management included the decisions on information acquisition procedures and tools as the company know what data should be selected, the way it should be selected, method to be used by organization by assigning responsible person for forecastingdemand parts and their role, ways, cross function cooperation and collaboration between company in order to create a shared forecast by gathering different kinds of information within the company or supply network, joint development of forecasts and assessment of accuracy by selecting the appropriate metric and determine proper incentive mechanisms (Danese and Kalchschmidt, 2011). However, some research claimed that the common approach to forecastingdemand parts depends on defining a demandforecasting unit (DFU) and evaluate historical information to define the average, cyclical, trend and seasonal demand parts (SAP, 1996).
ratio. We calculate the percentage of improvement by comparing the average regret values of the data-driven (under policy π) and Holt-Winters methods. The percentage of improvement is a convex function of the critical ratio and reaches the minimum value at 0.5, which is the point where under- and over-shooting the demand are penalized equally. Therefore, the integrated forecasting and inventory control loses its attractiveness since bare forecasting achieves the same performance. However, when the imbalance is high (in many practical applications, backorder costs are much higher than holding costs), the cost reduction becomes very significant: up to 90% cost reduction is observed at extreme critical ratio values (Figure 4).
Statistical analysis based on DSARIMA model is suitable with electrical power characteristics with continuous and fluctuating load patterns. The load changes are always unexpected at any time depending on electrical power demand in the load center. With the statistical analysis model, predictions are able to generate data that is not included in the data training process. Through the best model assumption, the model in this study was able to predict with the average accuracy of MAPE of 2.06%. Further research that can be developed is the pattern of electrical power demand on a large-scale area such as Java-Madura-Bali, Indonesia electricity network interconnection.
The demand of electricity forms the basis for power system planning, power security and supply reliability. The need for forecasting models that evaluate the electric consumption with the highest level of accuracy is underlined by the black-outs for the whole Malaysia that occurred in 2005. The relevance of forecastingdemand for the utility company has become a much-discussed issue in the recent years which led to the development of new tools and methods for forecasting in the last two decades  . The issue of statistical forecasting versus non statistical forecasting or judgmental method of forecasting and decision making has been the focus of many debates for the past decades. It does become an issue too for Malaysian utility company in implementing their forecasting practices. The proponent of statistical techniques is stressing the importance of accuracy in forecast and consistency without the element of human variation and biasness. Bunn and Wright  explore the issues of quality of judgmental forecasts, judgmental adjustment of statistical forecasts and the practice of combining statistical approach and judgmental techniques for improving forecast accuracy. It is the current practice of utility company to employed short term forecast which is purely based on the expertise and experience of one forecaster. Through experience, the experts developed intuitive relationships between electrical load and weather parameters, time of day, day of week, season and time lag of response. Various factors need to be taken into account in order to arrive at hourly, daily and weekly forecast. These factors are daily temperature, legal and religious holidays, seasonal effects and human behavior whether they will take a day off preceding and following the holidays as to take advantage of a long break. Modifications in the electricity usage patterns are observed during these times as people have the tendencies of creating long weekend. Short term forecast based on the experienced forecaster is highly reliable with forecast error in the range of two to three percents. Lawrence and O’Connor [3,4] compared several statistical forecasting methods from naïve forecasts to an average judgment forecasts.
The role of accounting and auditing services is crucial to the well-being of small business entities. While accounting guarantees the dissemination of vital information about the accountability and trust of the business entities to investors and the public, auditing on the other hand lends credibility to financial statements of the entities. Unfortunately, hiring an auditor for quality audits and having the very best internal controls can be very expensive, especially for small businesses. The question of how much money should be paid to a professional accountant to provide services relating to auditing and internal controls is a matter of perspective and circumstances. The wrong perception and inexperience of small business owners, operators and managers on the audit of a business constitute a negative impact on auditors in providing accounting and audit services to smallscale business in Nigeria. The fear of audits is also a major characteristic of small business owners that inhibits possible audits of small businesses. We therefore can conclude from the study that smallscale businesses do not demand for accounting and audit services in Nigeria.
The sale of new homes would likely not grow significantly in 2010. Even though interest rates, a key indicator, continue to drop, unemployment is also rising. Therefore, a predicted demand of 325,000 to 335,000 is reasonable. (Sales dropped by 20,000 in 2009 primarily due to a significant increase in unemployment, even though mortgage rates (a key indicator for house sales) dropped to 4.00%. Interest rates will continue to drop in 2010 but unemployment will edge upwards; therefore a change ranging from a slight decrease of 5,000 to a slight increase of 5,000 is likely.)