Keywords |
| Neural Network, Fuzzy logic, Load forecasting. |
INTRODUCTION |
| FORECASTING for vision load demand prerequisite is the most imperative key for power system planning. The ability of the generation, transmission and distribution capacities are strictly dependant on the precise energy and load forecasting for that system Power system expansion planning starts with a forecast of anticipated future load requirements. Estimates of both demand and energy requirements are crucial to valuable system planning. The term forecast refers to projected load requirements determined using a systematic process of defining future loads in sufficient quantitative detail to permit important system expansion decisions to be made. The Energy Management System demands accurate load forecasting and short term Load Forecasting provides better and truthful results [1]. |
| 预测飞机负荷预测有很多es of method are there in the present industry. The fore casting may do based on the time period. Long Term Forecasting, in this type of forecasting the forecasting period is long may be for 10 years. The planning of maintenance, scheduling of the fuel supply etc. calls for medium term load forecast. The medium term load forecast covers a period of a few weeks. It provides the peak load and the daily energy requirement. Another type of forecasting is Short Term Forecasting. Short term Forecasting gives the accurate values to predict the load demand. This short term forecasting covers to predict the load for a short term period of one week. The method that has been hassled upon is Short term load forecasting. A number of methods and techniques have already been worked out for prediction of load such as Artificial Neural Networks (ANN), Fuzzy Logic, and Regression Methods etc. Among all these Neural Networks are having the properties of sluggish convergence time and deprived ability to process a large number of variables at a time. In a power system with large geographical area, the weather and electricity demand diversity across the entire area is a key issue that influences the forecasting accuracy. In general the load will fluctuate more based on the weather conditions of the area. These weather conditions give the non linear behavior of the load. In day times the load is different from the night times, also in the week days and the normal days, the seasons will highly effect the load variations. |
II. THE WORK |
| In this study, a short term load forecasting method using fuzzy logic has been developed and a tender to the progression of the study with the use of artificial neural network (ANN) in different ways has been put up. A part of complete and generalized software by means of Fuzzy Logic has been attempt to put into survival to forecast electrical load for domestic as well as commercial areas such as industries, institute or residential colonies etc. The input parameters are Day’s minimum temperature, Day’s maximum temperature, season, day capacity, rain, daylight intensity (Cloudy). Day’s minimum temperature is a temperature at what time operational hours start. All these parameters are put as input to fuzzy system and the inputs are first of all level in the requisite value restrictions and fuzzified. preceding data (historical data or heuristic knowledge) which has already been stored in data bottom is used for deduction. Rule base is designed to follow the heuristic knowledge according to the membership functions of a variety of inputs. As in Fig. 1, Degree of Membership for different input parameters is establish out in the range [0-1] and then defuzzified to get the crisp output which is then de-scaled to the required units and range [8], [10]. |
| The main work we under this section is collecting the data of the university load conditions and we had taken the temperature conditions also by keeping some temperature equipments, to find the temperature conditions. |
III. HISTORICAL DATA AND KEY FACTORS |
| A good quality of past data for contribution parameters for the last few years has been stored in data base organization system (DBMS) for exact load forecasting [5]. Short term load forecasting mostly depends on the subsequent circumstances: |
| Utilization of electric power in a day |
| Temperature and weather conditions |
| Though the day capacity can be defined as working day or non working day (weekend or holiday). But as per this learn weekend and holiday are put in the similar category at what time no work or negligible work is done. One more category as special day has been considered. This is the category when work is done after usual 8 operational hours of the day (means if work is done for 9 Hrs. in a day shows one complete normal day and 1 Hr. of particular day) or 9 Hrs. of particular day depending on the type of work.We had considered the working hours of the ASTU in to two types. One based on the students learning process. First we consider teaching class only which include Theory and tutorials. The power consumption equipments are lights, projectors, and fans. And second session is practical sessions, which includes labs and workshops. |
| the type of work (either Theory or Practical) |
| Day elongation |
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| Two main factors have be distinct to decide climate circumstances – dull and/or Rainy weather. Cloudy weather gives an important effect of the day light intensity income more the clouds, lesser will be the day light concentration, more will be the utilization of electrical energy. These factors somehow are related to day’s minimum temperature and day’s greatest temperature. In fact, there can be a contrast between two working days with alike day ability but dissimilar climate conditions; load obsessive on both the days will be different. This can also occur that for two days, one is operational and additional is non working with dissimilar weather conditions, the load consumed is same [6]. |
IV. LOAD FORECASTING |
A. Fuzzification |
| Fuzzification is the process of making a crisp quantity fuzzy. We do this by simply recognizing that many of the quantities that we consider to be crisp and deterministic are actually not deterministic at all: They carry considerable uncertainty. If the form of indecision happens to arise because of imprecision, ambiguity, or vagueness, then the variable is probably fuzzy and can be represented by a membership function. In the real world, hardware such as a digital voltmeter generates crisp data, but these data are subject to experimental error. Fuzzy linguistic variables are used to stand for a variety of input as well as output parameters as the member of fuzzy sets. In arrange to articulate the fuzziness of in order, this manuscript create an agreement of fuzzy subsets for dissimilar inputs and outputs in total universe of dialogue as membership functions [9]. Membership functions can be symmetrical or asymmetrical. They are typically defined on one-dimensional universes, but they certainly can be described on multidimensional (or n-dimensional) universes. The association between several inputs and output may be non linear but linear membership functions have been used for straightforwardness and only the membership function for seasons is full as ridge-shaped membership function such as gbell mf, gauss mf, and gauss2mf. As in Fig. 2. |
| 一天的最低温度和最大的建立ure are represented as fuzzy subset [Very Low (VL), Low (L), Medium (M), High (H), and Very High (VH)]. |
| The linguistic variables of Day Capacity as [Minimum (min), Very Low (VL), Low (L), Medium (M), High (H), Very High (VH), Maximum (max)]. |
| The fuzzy subset for day capacity is [Very Low (VL), Low(L), Normal (N), High (H), Very High (VH)]. |
| The Season’s fuzzy subset is given with the names of season as [Spring, Summer, Autumn, Winter]. |
| The rain forecast has been given by fuzzy subset [No Rain, Drizzling, Normal Rain, Heavy Rain]. |
| Similarly, the output factor load also has been assigned as fuzzy subset with membership functions [Minimum (min), very low (VL), Low (L), medium (M), High (H), Very High (VH), Maximum (max)]. |
| B. Fuzzy Rule Base |
| This is the part of fuzzy system where heuristic knowledge is stored in terms of “IF-THEN Type” Rules. The rule base is used to send information to fuzzy inference system (FIS) to process through inference mechanism to numerically evaluate the information embedded in the fuzzy rule base to get the output. The rules are like: |
| IF (Min Temp is M) and (Max Temp is L) and (Day Light-Intensity (Clouds) is VH) and (Season (Day Number) is SUMMER) and (Rain is NORMAL) THEN (Output Load is H). |
| IF (Min Temp is H) and (Max Temp is H) and (Day Light- Intensity (Clouds) is L) and (Season (Day Number) is AUTUMN) and (Rain is DRIZZLING) THEN (Output Load is H). |
| IF (Min Temp is VL) and (Max Temp is VL) and (Day Light-Intensity (Clouds) is H) and (Season (Day Number) is WINTER) and (Rain is NO_RAIN) THEN (Output Load is MAX). |
| IF (Min Temp is H) and (Max Temp is H) and (Day Light-Intensity (Clouds) is L) and (Season (Day Number) is SPRING) and (Rain is NO_RAIN) THEN (Output Load is M). |
| IF (Day Type (Day Capacity) is MIN) and (Season (Day Number) is SUMMER) THEN (Output Load is MIN). |
| Table 1 shows the actual load, forecasted load and also the % error in the forecasted load for the data processed and calculated in the month of JUNE, 2005 (E.C). The % error of this study is calculated as |
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| The forecasted load compared with the actual load gives the maximum percentage error 4.04814% and minimum percentage error calculated is -0.38425%. The forecasted load for the month of October has been shown for the reason that this is the mid time of a working session in an Adama Science and Technology University in Ethiopia. Moreover, the change of season also takes place in this duration. |
| Both the results have been compared graphically, as in Fig. 3, showing the minute variations in the actual and forecasted loads for the same session given in Table I. |
VI. THE PROPOSED SCHEME |
| A single technique in itself is not sure to produce the good results, but it can be merged or supplemented with other methods also. Experiments were performed on some of the values using Artificial Neural Network (ANN) with Fuzzy Logic, giving a little reduction in the error but were not consistent due to lack of optimally trained network. In the proposed scheme, artificial neural network (ANN) can be added with fuzzy logic to produce better results in different configurations, provided, ANN is a trained network. Though ANN is slow in calculations and can not process number of inputs in large number of stages (hidden layers) [4], [7]. The two ways with which ANN can be added to fuzzy logic are shown as in Fig. 4 and Fig. 5. |
Tables at a glance |
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| Table 1 |
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Figures at a glance |
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| Figure 1 |
Figure 2 |
Figure 3 |
Figure 4 |
Figure 5 |
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References |
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